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    <title>My Weird Prompts: Audio &amp; Speech</title>
    <description><![CDATA[Speech recognition, TTS, voice cloning, audio engineering]]></description>
    <link>https://myweirdprompts.com/channel/audio-speech/</link>
    <language>en-us</language>
    <copyright>Copyright 2026 Daniel Rosehill</copyright>
    <lastBuildDate>Sat, 16 May 2026 20:43:15 GMT</lastBuildDate>
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    <image>
      <url>https://files.myweirdprompts.com/logos/mwp-square-3000.png</url>
      <title>My Weird Prompts: Audio &amp; Speech</title>
      <link>https://myweirdprompts.com/channel/audio-speech/</link>
    </image>

    <itunes:author>Daniel Rosehill</itunes:author>
    <itunes:summary><![CDATA[Speech recognition, TTS, voice cloning, audio engineering]]></itunes:summary>
    <itunes:owner>
      <itunes:name>Daniel Rosehill</itunes:name>
      <itunes:email>feed@myweirdprompts.com</itunes:email>
    </itunes:owner>
    <itunes:image href="https://files.myweirdprompts.com/logos/mwp-square-3000.png"/>
    <itunes:category text="Technology"/>
    <itunes:explicit>no</itunes:explicit>
    <itunes:type>episodic</itunes:type>
    <podcast:locked owner="feed@myweirdprompts.com">yes</podcast:locked>

    
    <item>
      <title>When Voice AI Features Enable Fraud</title>
      <description><![CDATA[Voice AI platforms now offer ambient sound layering, prosody control, and natural disfluencies—features that improve realism for legitimate calls but also empower scammers. This episode explores the architectural honesty problem, disclosure requirements, and what technical guardrails might actually work.]]></description>
      <link>https://myweirdprompts.com/episode/voice-ai-fraud-realism/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/voice-ai-fraud-realism/</guid>
      <pubDate>Tue, 12 May 2026 16:05:04 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/voice-ai-fraud-realism.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>When Voice AI Features Enable Fraud</itunes:title>
      <itunes:subtitle>Voice AI platforms now let you simulate background noise, hesitation, and natural conversation — and that&apos;s a problem.</itunes:subtitle>
      <itunes:summary><![CDATA[Voice AI platforms now offer ambient sound layering, prosody control, and natural disfluencies—features that improve realism for legitimate calls but also empower scammers. This episode explores the architectural honesty problem, disclosure requirements, and what technical guardrails might actually work.]]></itunes:summary>
      <itunes:duration>1966</itunes:duration>
      <itunes:episode>2781</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/voice-ai-fraud-realism.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/voice-ai-fraud-realism.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Dictation Setup Might Be Wrong</title>
      <description><![CDATA[Conventional wisdom says dictation accuracy depends on microphone quality, proximity, and a quiet room. But research into systems like Whisper tells a different story. Daniel Herman and Corn discuss how modern end-to-end neural ASR models are surprisingly robust to background noise, whispering, and fast speech — and why the single biggest predictor of accuracy is how well your audio matches the model's training distribution. They explore counterintuitive findings from Johns Hopkins, Carnegie Mellon, and ETH Zurich, including why language mismatch in background conversations can actually help, and how humans and machines co-evolve through computer-directed speech. If you dictate text, this episode will change how you think about your setup.]]></description>
      <link>https://myweirdprompts.com/episode/dictation-accurary-myths-whisper/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/dictation-accurary-myths-whisper/</guid>
      <pubDate>Mon, 11 May 2026 07:46:11 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/dictation-accurary-myths-whisper.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Why Your Dictation Setup Might Be Wrong</itunes:title>
      <itunes:subtitle>Modern ASR is shockingly robust. The biggest predictor of accuracy? How well your audio matches its training data.</itunes:subtitle>
      <itunes:summary><![CDATA[Conventional wisdom says dictation accuracy depends on microphone quality, proximity, and a quiet room. But research into systems like Whisper tells a different story. Daniel Herman and Corn discuss how modern end-to-end neural ASR models are surprisingly robust to background noise, whispering, and fast speech — and why the single biggest predictor of accuracy is how well your audio matches the model's training distribution. They explore counterintuitive findings from Johns Hopkins, Carnegie Mellon, and ETH Zurich, including why language mismatch in background conversations can actually help, and how humans and machines co-evolve through computer-directed speech. If you dictate text, this episode will change how you think about your setup.]]></itunes:summary>
      <itunes:duration>2170</itunes:duration>
      <itunes:episode>2754</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/dictation-accurary-myths-whisper.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/dictation-accurary-myths-whisper.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Foot Pedals vs USB Buttons: The Ergonomics of Dictation</title>
      <description><![CDATA[When you trigger dictation hundreds of times a day, the choice between a foot pedal and a USB button becomes an ergonomic puzzle. This episode explores the surprising engineering and design trade-offs behind the perfect dictation trigger.]]></description>
      <link>https://myweirdprompts.com/episode/dictation-trigger-hardware-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/dictation-trigger-hardware-guide/</guid>
      <pubDate>Fri, 08 May 2026 13:00:59 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/dictation-trigger-hardware-guide.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Foot Pedals vs USB Buttons: The Ergonomics of Dictation</itunes:title>
      <itunes:subtitle>Foot pedals, USB buttons, and under-desk macro pads for voice dictation — a deep dive into the hardware that makes AI dictation work.</itunes:subtitle>
      <itunes:summary><![CDATA[When you trigger dictation hundreds of times a day, the choice between a foot pedal and a USB button becomes an ergonomic puzzle. This episode explores the surprising engineering and design trade-offs behind the perfect dictation trigger.]]></itunes:summary>
      <itunes:duration>1954</itunes:duration>
      <itunes:episode>2707</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/dictation-trigger-hardware-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/dictation-trigger-hardware-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Text Normalization&apos;s Hidden Complexity</title>
      <description><![CDATA[When a TTS engine reads 'WHAT' as 'W-H-A-T', the problem isn't acronyms—it's text normalization. This episode explores the engineering challenge of disambiguating raw text for speech synthesis, from rule-based regex to BERT sidecars and markup schemas.]]></description>
      <link>https://myweirdprompts.com/episode/tts-acronym-handling-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tts-acronym-handling-pipeline/</guid>
      <pubDate>Sun, 03 May 2026 13:17:52 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tts-acronym-handling-pipeline.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Text Normalization&apos;s Hidden Complexity</itunes:title>
      <itunes:subtitle>How to handle acronyms in text-to-speech pipelines using BERT models, lexicons, and layered preprocessing.</itunes:subtitle>
      <itunes:summary><![CDATA[When a TTS engine reads 'WHAT' as 'W-H-A-T', the problem isn't acronyms—it's text normalization. This episode explores the engineering challenge of disambiguating raw text for speech synthesis, from rule-based regex to BERT sidecars and markup schemas.]]></itunes:summary>
      <itunes:duration>2264</itunes:duration>
      <itunes:episode>2618</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tts-acronym-handling-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tts-acronym-handling-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Letting Non-Experts Direct Audio Tools Through Conversation</title>
      <description><![CDATA[This episode explores how agentic AI can empower non-engineers to master spoken word audio by directing complex tools through conversation, rather than replacing professionals. It demystifies mastering while revealing what AI is actually useful for today.]]></description>
      <link>https://myweirdprompts.com/episode/audio-mastering-ai-agents/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/audio-mastering-ai-agents/</guid>
      <pubDate>Sat, 02 May 2026 13:33:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/audio-mastering-ai-agents.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Letting Non-Experts Direct Audio Tools Through Conversation</itunes:title>
      <itunes:subtitle>How to use AI for podcast mastering — and why agentic AI works better for small tasks than big promises.</itunes:subtitle>
      <itunes:summary><![CDATA[This episode explores how agentic AI can empower non-engineers to master spoken word audio by directing complex tools through conversation, rather than replacing professionals. It demystifies mastering while revealing what AI is actually useful for today.]]></itunes:summary>
      <itunes:duration>2283</itunes:duration>
      <itunes:episode>2602</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/audio-mastering-ai-agents.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/audio-mastering-ai-agents.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Decoupling Script from Voice</title>
      <description><![CDATA[What if listeners could choose any voice for a podcast host? We explore the technical feasibility of dynamic voice replacement, from voice cloning embeddings to on-device rendering, and what it means for the future of personalized audio.]]></description>
      <link>https://myweirdprompts.com/episode/personalized-podcast-voices/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/personalized-podcast-voices/</guid>
      <pubDate>Sat, 02 May 2026 09:47:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/personalized-podcast-voices.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Decoupling Script from Voice</itunes:title>
      <itunes:subtitle>How dynamic voice replacement could let listeners choose who narrates each host&apos;s lines.</itunes:subtitle>
      <itunes:summary><![CDATA[What if listeners could choose any voice for a podcast host? We explore the technical feasibility of dynamic voice replacement, from voice cloning embeddings to on-device rendering, and what it means for the future of personalized audio.]]></itunes:summary>
      <itunes:duration>1870</itunes:duration>
      <itunes:episode>2591</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/personalized-podcast-voices.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/personalized-podcast-voices.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Uncanny Valley of Clean Speech</title>
      <description><![CDATA[When removing 'um's from audio makes you sound less human, and keeping them makes you sound less polished. This episode explores the sweet spot between polish and naturalness in speech editing pipelines.]]></description>
      <link>https://myweirdprompts.com/episode/disfluency-detection-speech-cleaning/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/disfluency-detection-speech-cleaning/</guid>
      <pubDate>Sat, 02 May 2026 09:24:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/disfluency-detection-speech-cleaning.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>The Uncanny Valley of Clean Speech</itunes:title>
      <itunes:subtitle>How transformer models distinguish &quot;um&quot; from meaningful speech — and why removing too much makes you sound like a robot.</itunes:subtitle>
      <itunes:summary><![CDATA[When removing 'um's from audio makes you sound less human, and keeping them makes you sound less polished. This episode explores the sweet spot between polish and naturalness in speech editing pipelines.]]></itunes:summary>
      <itunes:duration>1699</itunes:duration>
      <itunes:episode>2590</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/disfluency-detection-speech-cleaning.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/disfluency-detection-speech-cleaning.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>What Your Browser Does to Mic Audio Before It Reaches Your Server</title>
      <description><![CDATA[Most developers copy-paste the getUserMedia snippet from MDN, wire up a MediaRecorder, and never think about it again. But what's actually happening under the hood varies wildly across browsers. This episode unpacks the hidden audio pipeline in Chrome, Firefox, and Safari — from default sample rates that drop to 8 kHz on mobile, to Opus codec quirks where higher bitrates can actually hurt transcription accuracy. We explore the constraints API (which is a polite request, not a command), the destructive effects of echo cancellation and noise suppression on speech-to-text, and the practical tools like RecordRTC and Web Audio API for taking back control. If you're building a browser-based recording app and wondering why transcription quality varies between users, this is the episode for you.]]></description>
      <link>https://myweirdprompts.com/episode/browser-mic-audio-pipeline/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/browser-mic-audio-pipeline/</guid>
      <pubDate>Fri, 01 May 2026 11:55:27 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/browser-mic-audio-pipeline.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>What Your Browser Does to Mic Audio Before It Reaches Your Server</itunes:title>
      <itunes:subtitle>getUserMedia returns audio, but not raw audio. Here&apos;s what browsers actually do to your mic feed before it hits your server.</itunes:subtitle>
      <itunes:summary><![CDATA[Most developers copy-paste the getUserMedia snippet from MDN, wire up a MediaRecorder, and never think about it again. But what's actually happening under the hood varies wildly across browsers. This episode unpacks the hidden audio pipeline in Chrome, Firefox, and Safari — from default sample rates that drop to 8 kHz on mobile, to Opus codec quirks where higher bitrates can actually hurt transcription accuracy. We explore the constraints API (which is a polite request, not a command), the destructive effects of echo cancellation and noise suppression on speech-to-text, and the practical tools like RecordRTC and Web Audio API for taking back control. If you're building a browser-based recording app and wondering why transcription quality varies between users, this is the episode for you.]]></itunes:summary>
      <itunes:duration>1873</itunes:duration>
      <itunes:episode>2582</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/browser-mic-audio-pipeline.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/browser-mic-audio-pipeline.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Audio Fingerprinting Actually Works</title>
      <description><![CDATA[Most people know audio fingerprinting as the magic behind Shazam and YouTube Content ID, but the actual mechanics are surprisingly elegant. This episode breaks down the entire pipeline step by step: how a short-time Fourier transform turns audio into a spectrogram, how peak picking filters out noise and compression artifacts, and how constellation maps and hash pairs enable near-instant matching against millions of songs. We also explore a concrete meta-example: how the My Weird Prompts production pipeline uses the same technique to locate fixed audio segments in variable-length TTS output — without relying on timestamps at all.]]></description>
      <link>https://myweirdprompts.com/episode/audio-fingerprinting-mechanics-shazam/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/audio-fingerprinting-mechanics-shazam/</guid>
      <pubDate>Fri, 01 May 2026 08:45:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/audio-fingerprinting-mechanics-shazam.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>How Audio Fingerprinting Actually Works</itunes:title>
      <itunes:subtitle>Spectrogram peaks, constellation maps, and hash matching — the elegant mechanics behind identifying any song in seconds.</itunes:subtitle>
      <itunes:summary><![CDATA[Most people know audio fingerprinting as the magic behind Shazam and YouTube Content ID, but the actual mechanics are surprisingly elegant. This episode breaks down the entire pipeline step by step: how a short-time Fourier transform turns audio into a spectrogram, how peak picking filters out noise and compression artifacts, and how constellation maps and hash pairs enable near-instant matching against millions of songs. We also explore a concrete meta-example: how the My Weird Prompts production pipeline uses the same technique to locate fixed audio segments in variable-length TTS output — without relying on timestamps at all.]]></itunes:summary>
      <itunes:duration>1568</itunes:duration>
      <itunes:episode>2563</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/audio-fingerprinting-mechanics-shazam.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/audio-fingerprinting-mechanics-shazam.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Base64 Adds 33% Overhead (And Why You Still Need It)</title>
      <description><![CDATA[Base64 isn't compression—it's safety. This episode explains why audio pipelines rely on a text-safe encoding that inflates file size, and when to choose streaming over batch processing for voice APIs.]]></description>
      <link>https://myweirdprompts.com/episode/base64-audio-api-limits/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/base64-audio-api-limits/</guid>
      <pubDate>Thu, 30 Apr 2026 07:46:07 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/base64-audio-api-limits.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Why Base64 Adds 33% Overhead (And Why You Still Need It)</itunes:title>
      <itunes:subtitle>Base64 isn’t compression — it’s a safe transport encoding. Here’s how it works with audio APIs and where its limits are.</itunes:subtitle>
      <itunes:summary><![CDATA[Base64 isn't compression—it's safety. This episode explains why audio pipelines rely on a text-safe encoding that inflates file size, and when to choose streaming over batch processing for voice APIs.]]></itunes:summary>
      <itunes:duration>2237</itunes:duration>
      <itunes:episode>2543</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/base64-audio-api-limits.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/base64-audio-api-limits.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Speech-to-Speech Models Eliminate the Robot Voice</title>
      <description><![CDATA[Why do so many AI voice agents still feel like talking to a robot? This episode unpacks the architectural difference between traditional pipeline systems (ASR → LLM → TTS) and the new class of natively integrated speech-to-speech models. We explore how the text bottleneck destroys prosody and emotion, why cumulative latency breaks conversational rhythm, and how models like OpenAI's Realtime API, Moshi, and Hume's EVI process audio end-to-end. We also cover the trade-offs between elegance and production readiness, and why pipeline tools still dominate despite their seams.]]></description>
      <link>https://myweirdprompts.com/episode/speech-to-speech-models-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/speech-to-speech-models-explained/</guid>
      <pubDate>Wed, 29 Apr 2026 00:27:17 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/speech-to-speech-models-explained.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>How Speech-to-Speech Models Eliminate the Robot Voice</itunes:title>
      <itunes:subtitle>Why AI voice agents sound robotic, and how natively integrated speech-to-speech models fix it.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do so many AI voice agents still feel like talking to a robot? This episode unpacks the architectural difference between traditional pipeline systems (ASR → LLM → TTS) and the new class of natively integrated speech-to-speech models. We explore how the text bottleneck destroys prosody and emotion, why cumulative latency breaks conversational rhythm, and how models like OpenAI's Realtime API, Moshi, and Hume's EVI process audio end-to-end. We also cover the trade-offs between elegance and production readiness, and why pipeline tools still dominate despite their seams.]]></itunes:summary>
      <itunes:duration>1688</itunes:duration>
      <itunes:episode>2512</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/speech-to-speech-models-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/speech-to-speech-models-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Design That Makes Voice Agents Tolerable</title>
      <description><![CDATA[Beyond cold calls and hype, what separates voice AI people actually choose to use from ones they avoid? This episode examines the design principles—opt-in automation, conversational markers, and backend agency—that make drive-thru ordering, healthcare triage, and accessibility tools genuinely work.]]></description>
      <link>https://myweirdprompts.com/episode/real-world-voice-ai-deployments/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/real-world-voice-ai-deployments/</guid>
      <pubDate>Wed, 29 Apr 2026 00:04:06 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/real-world-voice-ai-deployments.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>The Design That Makes Voice Agents Tolerable</itunes:title>
      <itunes:subtitle>Drive-thru accuracy, healthcare triage, and the design secret that makes people *want* to talk to a machine.</itunes:subtitle>
      <itunes:summary><![CDATA[Beyond cold calls and hype, what separates voice AI people actually choose to use from ones they avoid? This episode examines the design principles—opt-in automation, conversational markers, and backend agency—that make drive-thru ordering, healthcare triage, and accessibility tools genuinely work.]]></itunes:summary>
      <itunes:duration>1680</itunes:duration>
      <itunes:episode>2510</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/real-world-voice-ai-deployments.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/real-world-voice-ai-deployments.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Noise Reduction Can Ruin Transcription Accuracy</title>
      <description><![CDATA[Most developers assume cleaner audio means better transcription — but the research shows the opposite. This episode explores the noise reduction paradox: why modern ASR models actually perform worse on denoised audio, and how to build a pipeline that serves both transcription accuracy and podcast-quality output. We break down the algorithm landscape from heavyweight machine learning to ultra-lightweight DSP hybrids, explain why babble noise and Irish accents create special challenges, and lay out a two-path architecture that optimizes for each use case separately. If you're building a voice app and wondering whether to clean audio before or after transcription, this episode will save you weeks of trial and error.]]></description>
      <link>https://myweirdprompts.com/episode/noise-reduction-transcription-paradox/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/noise-reduction-transcription-paradox/</guid>
      <pubDate>Mon, 27 Apr 2026 11:33:31 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/noise-reduction-transcription-paradox.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Why Noise Reduction Can Ruin Transcription Accuracy</itunes:title>
      <itunes:subtitle>Cleaning audio before transcription can increase errors by up to 46%. Here&apos;s the right approach for your voice app.</itunes:subtitle>
      <itunes:summary><![CDATA[Most developers assume cleaner audio means better transcription — but the research shows the opposite. This episode explores the noise reduction paradox: why modern ASR models actually perform worse on denoised audio, and how to build a pipeline that serves both transcription accuracy and podcast-quality output. We break down the algorithm landscape from heavyweight machine learning to ultra-lightweight DSP hybrids, explain why babble noise and Irish accents create special challenges, and lay out a two-path architecture that optimizes for each use case separately. If you're building a voice app and wondering whether to clean audio before or after transcription, this episode will save you weeks of trial and error.]]></itunes:summary>
      <itunes:duration>1647</itunes:duration>
      <itunes:episode>2486</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/noise-reduction-transcription-paradox.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/noise-reduction-transcription-paradox.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Screaming Baby Stress Test</title>
      <description><![CDATA[When a baby's wail hits 110 decibels, most noise-cancelling headsets fail. We test whether the Oleap Archer's 50dB AI ClearTalk can handle the ultimate real-world challenge—and whether off-the-shelf dictation tools or a custom pipeline win when your hands are full.]]></description>
      <link>https://myweirdprompts.com/episode/hands-free-dictation-baby-noise/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/hands-free-dictation-baby-noise/</guid>
      <pubDate>Mon, 27 Apr 2026 10:40:49 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/hands-free-dictation-baby-noise.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>The Screaming Baby Stress Test</itunes:title>
      <itunes:subtitle>Choosing the right headset and control method for dictation when you&apos;re holding a baby who won&apos;t stop screaming.</itunes:subtitle>
      <itunes:summary><![CDATA[When a baby's wail hits 110 decibels, most noise-cancelling headsets fail. We test whether the Oleap Archer's 50dB AI ClearTalk can handle the ultimate real-world challenge—and whether off-the-shelf dictation tools or a custom pipeline win when your hands are full.]]></itunes:summary>
      <itunes:duration>1506</itunes:duration>
      <itunes:episode>2479</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/hands-free-dictation-baby-noise.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/hands-free-dictation-baby-noise.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Podcast RSS Feeds Can Speak Every Language</title>
      <description><![CDATA[Over 460 million people listen to podcasts monthly, but most shows are locked in a single language. This episode explores how the Podcasting 2.0 namespace, transcript tags with timing data, and the proposed "alternative enclosure" tag could let creators publish once and let apps handle localization. We break down the actual XML plumbing, the cost per language (roughly $1 for translation + $15 for TTS generation), and why the shift from server-side to client-side localization changes everything. Plus: how voice cloning tools like ElevenLabs now support 29 languages at 95%+ intelligibility, and why the "podcast:voice" tag could let creators specify custom voice profiles for each language.]]></description>
      <link>https://myweirdprompts.com/episode/multilingual-podcast-rss-feeds/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/multilingual-podcast-rss-feeds/</guid>
      <pubDate>Sun, 26 Apr 2026 10:17:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/multilingual-podcast-rss-feeds.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>How Podcast RSS Feeds Can Speak Every Language</itunes:title>
      <itunes:subtitle>One RSS feed, a transcript tag, and TTS voice cloning — the emerging standard for letting any podcast speak any language.</itunes:subtitle>
      <itunes:summary><![CDATA[Over 460 million people listen to podcasts monthly, but most shows are locked in a single language. This episode explores how the Podcasting 2.0 namespace, transcript tags with timing data, and the proposed "alternative enclosure" tag could let creators publish once and let apps handle localization. We break down the actual XML plumbing, the cost per language (roughly $1 for translation + $15 for TTS generation), and why the shift from server-side to client-side localization changes everything. Plus: how voice cloning tools like ElevenLabs now support 29 languages at 95%+ intelligibility, and why the "podcast:voice" tag could let creators specify custom voice profiles for each language.]]></itunes:summary>
      <itunes:duration>1659</itunes:duration>
      <itunes:episode>2443</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/multilingual-podcast-rss-feeds.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/multilingual-podcast-rss-feeds.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Diarization Fails Silently</title>
      <description><![CDATA[Speaker diarization errors are invisible but costly—mislabeling a call center agent or collapsing a courtroom transcript. This episode explores why the pipeline breaks and how to build robust systems that catch what the tools hide.]]></description>
      <link>https://myweirdprompts.com/episode/speaker-diarization-deep-dive/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/speaker-diarization-deep-dive/</guid>
      <pubDate>Sun, 19 Apr 2026 21:28:28 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/speaker-diarization-deep-dive.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>When Diarization Fails Silently</itunes:title>
      <itunes:subtitle>Discover how PyAnnote and other tools tackle the critical task of identifying &quot;who spoke when&quot; in audio—and why it’s harder than it sounds.</itunes:subtitle>
      <itunes:summary><![CDATA[Speaker diarization errors are invisible but costly—mislabeling a call center agent or collapsing a courtroom transcript. This episode explores why the pipeline breaks and how to build robust systems that catch what the tools hide.]]></itunes:summary>
      <itunes:duration>1565</itunes:duration>
      <itunes:episode>2337</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/speaker-diarization-deep-dive.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/speaker-diarization-deep-dive.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Danish AI: Bridging the Localization Gap</title>
      <description><![CDATA[What does AI look like for Danish speakers in 2026? With six million native speakers, Danish serves as a stress test for AI localization in smaller languages. From chatbots to speech-to-text and text-to-speech systems, this episode dives into the unique challenges Danish poses, from its complex phonology to healthcare applications. Discover why even a high-resourced language like Danish struggles to match English AI tools and what this means for dozens of other under-resourced languages worldwide.]]></description>
      <link>https://myweirdprompts.com/episode/danish-ai-localization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/danish-ai-localization/</guid>
      <pubDate>Sun, 19 Apr 2026 00:05:15 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/danish-ai-localization.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Danish AI: Bridging the Localization Gap</itunes:title>
      <itunes:subtitle>How does AI handle Danish? Explore the challenges and progress in making AI tools work for small-language populations.</itunes:subtitle>
      <itunes:summary><![CDATA[What does AI look like for Danish speakers in 2026? With six million native speakers, Danish serves as a stress test for AI localization in smaller languages. From chatbots to speech-to-text and text-to-speech systems, this episode dives into the unique challenges Danish poses, from its complex phonology to healthcare applications. Discover why even a high-resourced language like Danish struggles to match English AI tools and what this means for dozens of other under-resourced languages worldwide.]]></itunes:summary>
      <itunes:duration>2568</itunes:duration>
      <itunes:episode>2311</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/danish-ai-localization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/danish-ai-localization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Invisible Gatekeeper of Voice Tech</title>
      <description><![CDATA[Voice activity detection (VAD) is the unsung hero—or villain—of every voice tech system. It decides whether your voice assistant hears you or ignores you, and its failures are often invisible. This episode dives into the key players in VAD, from WebRTC and Silero to Picovoice Cobra and Whisper wrappers, and explores why this seemingly simple task remains an active research problem. We’ll also uncover why VAD is uniquely suited to run efficiently on CPUs and how its challenges are shaped by evolving use cases like streaming voice assistants and edge devices.]]></description>
      <link>https://myweirdprompts.com/episode/voice-activity-detection/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/voice-activity-detection/</guid>
      <pubDate>Fri, 17 Apr 2026 20:31:25 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/voice-activity-detection.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>The Invisible Gatekeeper of Voice Tech</itunes:title>
      <itunes:subtitle>How voice activity detection shapes every step of the voice tech pipeline, and why it’s harder than it seems.</itunes:subtitle>
      <itunes:summary><![CDATA[Voice activity detection (VAD) is the unsung hero—or villain—of every voice tech system. It decides whether your voice assistant hears you or ignores you, and its failures are often invisible. This episode dives into the key players in VAD, from WebRTC and Silero to Picovoice Cobra and Whisper wrappers, and explores why this seemingly simple task remains an active research problem. We’ll also uncover why VAD is uniquely suited to run efficiently on CPUs and how its challenges are shaped by evolving use cases like streaming voice assistants and edge devices.]]></itunes:summary>
      <itunes:duration>1548</itunes:duration>
      <itunes:episode>2288</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/voice-activity-detection.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/voice-activity-detection.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The AI Transcription Sweet Spot</title>
      <description><![CDATA[Conventional wisdom says more data equals better AI performance. But new experiments show that for speech-to-text models like Whisper, higher audio bitrates can actually increase error rates. We dive into the surprising U-shaped curve of transcription accuracy, explore why models perform best on "messy" web-quality audio, and uncover the massive cost savings for anyone processing audio at scale. Learn the optimal bitrate for your pipeline and why aligning with a model's training data is more important than pristine quality.]]></description>
      <link>https://myweirdprompts.com/episode/audio-bitrate-ai-transcription/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/audio-bitrate-ai-transcription/</guid>
      <pubDate>Fri, 17 Apr 2026 13:03:16 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/audio-bitrate-ai-transcription.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>The AI Transcription Sweet Spot</itunes:title>
      <itunes:subtitle>Does higher-quality audio make AI transcription worse? New research reveals a surprising &quot;sweet spot&quot; for bitrate, challenging a core assumption of...</itunes:subtitle>
      <itunes:summary><![CDATA[Conventional wisdom says more data equals better AI performance. But new experiments show that for speech-to-text models like Whisper, higher audio bitrates can actually increase error rates. We dive into the surprising U-shaped curve of transcription accuracy, explore why models perform best on "messy" web-quality audio, and uncover the massive cost savings for anyone processing audio at scale. Learn the optimal bitrate for your pipeline and why aligning with a model's training data is more important than pristine quality.]]></itunes:summary>
      <itunes:duration>1347</itunes:duration>
      <itunes:episode>2272</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/audio-bitrate-ai-transcription.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/audio-bitrate-ai-transcription.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Making Voice Agents Feel Natural</title>
      <description><![CDATA[Voice transcription and synthesis sound great, but talking to a voice agent still feels slightly off. Why? Because the hard problems are invisible: how agents detect when you've actually finished speaking versus just pausing to think, how they handle interruptions without cutting you off mid-sentence, what happens when latency budgets blow, and whether they can read emotional tone. This episode digs into the conversational dynamics underneath voice AI—the failure modes most developers don't fully understand—and maps the engineering solutions emerging across Vapi, LiveKit, Pipecat, Deepgram, and others. Turn-taking isn't solved. Here's what solving it actually requires.]]></description>
      <link>https://myweirdprompts.com/episode/voice-agent-conversation-dynamics/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/voice-agent-conversation-dynamics/</guid>
      <pubDate>Sun, 12 Apr 2026 16:34:41 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/voice-agent-conversation-dynamics.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Making Voice Agents Feel Natural</itunes:title>
      <itunes:subtitle>Turn-taking, interruptions, and latency are destroying voice AI UX—and the fixes are deeply technical. Here&apos;s what&apos;s actually happening underneath.</itunes:subtitle>
      <itunes:summary><![CDATA[Voice transcription and synthesis sound great, but talking to a voice agent still feels slightly off. Why? Because the hard problems are invisible: how agents detect when you've actually finished speaking versus just pausing to think, how they handle interruptions without cutting you off mid-sentence, what happens when latency budgets blow, and whether they can read emotional tone. This episode digs into the conversational dynamics underneath voice AI—the failure modes most developers don't fully understand—and maps the engineering solutions emerging across Vapi, LiveKit, Pipecat, Deepgram, and others. Turn-taking isn't solved. Here's what solving it actually requires.]]></itunes:summary>
      <itunes:duration>1717</itunes:duration>
      <itunes:episode>2183</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/voice-agent-conversation-dynamics.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/voice-agent-conversation-dynamics.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The TTS Developer&apos;s Dilemma: Size vs. Speed</title>
      <description><![CDATA[The text-to-speech landscape has exploded, leaving developers with a difficult choice: prioritize rich, emotional audio or lightning-fast response times? This episode dives deep into the technical architecture of modern TTS, from massive billion-parameter models to ultra-efficient edge runners. We explore how to balance GPU requirements, streaming capabilities, and bandwidth costs to build a voice experience that doesn't feel cheap. Plus, we tackle the nuances of prosody control, multilingual interference, and the battle against messy input text.]]></description>
      <link>https://myweirdprompts.com/episode/tts-model-latency-optimization/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tts-model-latency-optimization/</guid>
      <pubDate>Tue, 31 Mar 2026 12:15:05 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tts-model-latency-optimization.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>The TTS Developer&apos;s Dilemma: Size vs. Speed</itunes:title>
      <itunes:subtitle>Stop guessing. We break down the critical trade-offs between model size, latency, and sample rate for production-ready voice apps.</itunes:subtitle>
      <itunes:summary><![CDATA[The text-to-speech landscape has exploded, leaving developers with a difficult choice: prioritize rich, emotional audio or lightning-fast response times? This episode dives deep into the technical architecture of modern TTS, from massive billion-parameter models to ultra-efficient edge runners. We explore how to balance GPU requirements, streaming capabilities, and bandwidth costs to build a voice experience that doesn't feel cheap. Plus, we tackle the nuances of prosody control, multilingual interference, and the battle against messy input text.]]></itunes:summary>
      <itunes:duration>1634</itunes:duration>
      <itunes:episode>1809</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tts-model-latency-optimization.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tts-model-latency-optimization.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Architecture That Made AI Voices Run on a Raspberry Pi</title>
      <description><![CDATA[How did open-source models with 82 million parameters outperform billion-dollar AI? This episode explores the shift from cascaded pipelines to unified architectures, and why the future of voice synthesis might be tiny, private, and run on a $35 computer.]]></description>
      <link>https://myweirdprompts.com/episode/tiny-kokoro-voice-beats-giants/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/tiny-kokoro-voice-beats-giants/</guid>
      <pubDate>Tue, 31 Mar 2026 12:12:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/tiny-kokoro-voice-beats-giants.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>The Architecture That Made AI Voices Run on a Raspberry Pi</itunes:title>
      <itunes:subtitle>How a model the size of a tweet outperforms billion-dollar giants in the race for perfect AI speech.</itunes:subtitle>
      <itunes:summary><![CDATA[How did open-source models with 82 million parameters outperform billion-dollar AI? This episode explores the shift from cascaded pipelines to unified architectures, and why the future of voice synthesis might be tiny, private, and run on a $35 computer.]]></itunes:summary>
      <itunes:duration>1365</itunes:duration>
      <itunes:episode>1808</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/tiny-kokoro-voice-beats-giants.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/tiny-kokoro-voice-beats-giants.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Hacking the Brain&apos;s Alarm System</title>
      <description><![CDATA[Why do some sounds jolt us awake while others blend into dreams? This episode explores the dark art of designing emergency alerts that exploit the amygdala's ancient wiring, from military wake-up calls to smartphone banshees.]]></description>
      <link>https://myweirdprompts.com/episode/engineering-urgent-sound-alerts/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/engineering-urgent-sound-alerts/</guid>
      <pubDate>Tue, 31 Mar 2026 06:49:42 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/engineering-urgent-sound-alerts.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Hacking the Brain&apos;s Alarm System</itunes:title>
      <itunes:subtitle>Why some sounds make your skin crawl: the science of emergency alerts.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do some sounds jolt us awake while others blend into dreams? This episode explores the dark art of designing emergency alerts that exploit the amygdala's ancient wiring, from military wake-up calls to smartphone banshees.]]></itunes:summary>
      <itunes:duration>1288</itunes:duration>
      <itunes:episode>1800</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/engineering-urgent-sound-alerts.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/engineering-urgent-sound-alerts.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Audio Is the New &quot;Read Later&quot; Graveyard</title>
      <description><![CDATA[We explore why AI-generated audio is becoming the preferred way to consume technical content, turning the "Read Later" graveyard into a daily ritual. Discover the psychological benefits of conversational learning and how serverless GPU infrastructure makes high-quality synthesis economically viable. From RAG pipelines to the "fire hose with taps" model, we break down the architecture behind personalized educational feeds.]]></description>
      <link>https://myweirdprompts.com/episode/audio-vs-reading-educational-content/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/audio-vs-reading-educational-content/</guid>
      <pubDate>Mon, 30 Mar 2026 15:17:58 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/audio-vs-reading-educational-content.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Audio Is the New &quot;Read Later&quot; Graveyard</itunes:title>
      <itunes:subtitle>Why listening to AI conversations beats reading dense PDFs, and how serverless GPUs make it cheap.</itunes:subtitle>
      <itunes:summary><![CDATA[We explore why AI-generated audio is becoming the preferred way to consume technical content, turning the "Read Later" graveyard into a daily ritual. Discover the psychological benefits of conversational learning and how serverless GPU infrastructure makes high-quality synthesis economically viable. From RAG pipelines to the "fire hose with taps" model, we break down the architecture behind personalized educational feeds.]]></itunes:summary>
      <itunes:duration>2803</itunes:duration>
      <itunes:episode>1778</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/audio-vs-reading-educational-content.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/audio-vs-reading-educational-content.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Whisper Small Beats Whisper Large in Speed &amp; Accuracy</title>
      <description><![CDATA[A new benchmark on Ubuntu Linux using Handy and ONNX Runtime tested 13 speech-to-text models on a consumer AMD Radeon RX 7800 XT. The results reveal a surprising reality: OpenAI's massive Whisper Large model was nearly 3x slower and made 3 errors, while the tiny Whisper Small finished in under 1 second with zero errors. This episode explores why bigger isn't always better in AI, the "Goldilocks zone" of latency, and why streaming models might be the wrong tool for push-to-talk workflows.]]></description>
      <link>https://myweirdprompts.com/episode/whisper-small-beats-large-benchmark/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/whisper-small-beats-large-benchmark/</guid>
      <pubDate>Sun, 29 Mar 2026 16:01:33 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/whisper-small-beats-large-benchmark.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Whisper Small Beats Whisper Large in Speed &amp; Accuracy</itunes:title>
      <itunes:subtitle>A 4GPU benchmark on Ubuntu shows the 1.5B parameter Whisper Large is slower and less accurate than the tiny Whisper Small.</itunes:subtitle>
      <itunes:summary><![CDATA[A new benchmark on Ubuntu Linux using Handy and ONNX Runtime tested 13 speech-to-text models on a consumer AMD Radeon RX 7800 XT. The results reveal a surprising reality: OpenAI's massive Whisper Large model was nearly 3x slower and made 3 errors, while the tiny Whisper Small finished in under 1 second with zero errors. This episode explores why bigger isn't always better in AI, the "Goldilocks zone" of latency, and why streaming models might be the wrong tool for push-to-talk workflows.]]></itunes:summary>
      <itunes:duration>1618</itunes:duration>
      <itunes:episode>1752</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/whisper-small-beats-large-benchmark.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/whisper-small-beats-large-benchmark.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When AI Dubbing Swaps Your Gender</title>
      <description><![CDATA[Why does YouTube's auto-dub sometimes turn a man's voice into a woman's? We explore the glitchy reality of speech-to-speech AI, from lost prosody to the digital sandwich that flattens emotion.]]></description>
      <link>https://myweirdprompts.com/episode/youtube-auto-dubbing-architecture/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/youtube-auto-dubbing-architecture/</guid>
      <pubDate>Sun, 29 Mar 2026 02:51:36 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/youtube-auto-dubbing-architecture.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>When AI Dubbing Swaps Your Gender</itunes:title>
      <itunes:subtitle>How does YouTube translate a video with one click? We explore the tech behind auto-dubbing, from sandwich models to voice cloning.</itunes:subtitle>
      <itunes:summary><![CDATA[Why does YouTube's auto-dub sometimes turn a man's voice into a woman's? We explore the glitchy reality of speech-to-speech AI, from lost prosody to the digital sandwich that flattens emotion.]]></itunes:summary>
      <itunes:duration>1368</itunes:duration>
      <itunes:episode>1724</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/youtube-auto-dubbing-architecture.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/youtube-auto-dubbing-architecture.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Beyond Whisper: NVIDIA’s Real-Time Speech Revolution</title>
      <description><![CDATA[For years, OpenAI’s Whisper has been the gold standard for speech-to-text, but its batch-processing architecture creates a "latency floor" that hinders real-time interaction. This episode explores NVIDIA’s aggressive move into the ASR space with the Parakeet and Canary models, which utilize FastConformer and Token-and-Duration Transducer (TDT) architectures to achieve near-instantaneous results. We dive into why developers are ditching Whisper for 10x speed gains, the shift toward local inference on Apple Silicon, and how these specialized models are finally making the "digital sandwich" posture a thing of the past.]]></description>
      <link>https://myweirdprompts.com/episode/nvidia-parakeet-speech-recognition/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/nvidia-parakeet-speech-recognition/</guid>
      <pubDate>Thu, 26 Mar 2026 12:35:20 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/nvidia-parakeet-speech-recognition.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Beyond Whisper: NVIDIA’s Real-Time Speech Revolution</itunes:title>
      <itunes:subtitle>Move over Whisper. NVIDIA&apos;s new models offer 10x speed increases and better accuracy for real-time speech-to-text.</itunes:subtitle>
      <itunes:summary><![CDATA[For years, OpenAI’s Whisper has been the gold standard for speech-to-text, but its batch-processing architecture creates a "latency floor" that hinders real-time interaction. This episode explores NVIDIA’s aggressive move into the ASR space with the Parakeet and Canary models, which utilize FastConformer and Token-and-Duration Transducer (TDT) architectures to achieve near-instantaneous results. We dive into why developers are ditching Whisper for 10x speed gains, the shift toward local inference on Apple Silicon, and how these specialized models are finally making the "digital sandwich" posture a thing of the past.]]></itunes:summary>
      <itunes:duration>1171</itunes:duration>
      <itunes:episode>1555</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/nvidia-parakeet-speech-recognition.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/nvidia-parakeet-speech-recognition.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Architectural Divide Between Batch and Live Speech</title>
      <description><![CDATA[Why does the same AI model feel like a genius in batch transcription but a toddler in real-time voice typing? This episode unpacks the core difference: bidirectional context versus blindfolded guessing, and what it will take to make dictation seamless.]]></description>
      <link>https://myweirdprompts.com/episode/voice-typing-real-time-friction/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/voice-typing-real-time-friction/</guid>
      <pubDate>Sun, 15 Mar 2026 14:37:46 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/voice-typing-real-time-friction.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>The Architectural Divide Between Batch and Live Speech</itunes:title>
      <itunes:subtitle>Why does voice typing feel so clunky compared to recording a memo? We explore the technical hurdles of real-time AI transcription.</itunes:subtitle>
      <itunes:summary><![CDATA[Why does the same AI model feel like a genius in batch transcription but a toddler in real-time voice typing? This episode unpacks the core difference: bidirectional context versus blindfolded guessing, and what it will take to make dictation seamless.]]></itunes:summary>
      <itunes:duration>1577</itunes:duration>
      <itunes:episode>1218</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/voice-typing-real-time-friction.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/voice-typing-real-time-friction.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Pro Audio in Acoustic Nightmares: Mobile Recording Tips</title>
      <description><![CDATA[Tired of your podcast sounding like it was recorded in a tin can? Join Corn and Herman as they break down the ultimate mobile workflow for the spontaneous creator, from tackling the "acoustic nightmare" of hard stone walls to choosing the best USB-C microphones for your Android device. We explore why expensive gear won't fix a bad room and how simple household items like blankets and mattresses are often more effective than high-tech isolation booths.]]></description>
      <link>https://myweirdprompts.com/episode/mobile-recording-pro-audio-tips/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mobile-recording-pro-audio-tips/</guid>
      <pubDate>Thu, 05 Mar 2026 11:12:08 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mobile-recording-pro-audio-tips.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Pro Audio in Acoustic Nightmares: Mobile Recording Tips</itunes:title>
      <itunes:subtitle>Learn how to turn a marble-floored room into a studio using your phone, simple blankets, and the right USB-C gear.</itunes:subtitle>
      <itunes:summary><![CDATA[Tired of your podcast sounding like it was recorded in a tin can? Join Corn and Herman as they break down the ultimate mobile workflow for the spontaneous creator, from tackling the "acoustic nightmare" of hard stone walls to choosing the best USB-C microphones for your Android device. We explore why expensive gear won't fix a bad room and how simple household items like blankets and mattresses are often more effective than high-tech isolation booths.]]></itunes:summary>
      <itunes:duration>2179</itunes:duration>
      <itunes:episode>947</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mobile-recording-pro-audio-tips.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mobile-recording-pro-audio-tips.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>When Your Phone&apos;s Mic Beats Your Expensive Gear</title>
      <description><![CDATA[Why might your smartphone's internal microphone outperform dedicated external mics for AI transcription? We explore the surprising benchmarks, the role of proximity and processing, and how to choose gear that actually improves clarity for speech-to-text engines like Whisper.]]></description>
      <link>https://myweirdprompts.com/episode/mobile-audio-ai-transcription/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/mobile-audio-ai-transcription/</guid>
      <pubDate>Thu, 26 Feb 2026 16:55:56 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/mobile-audio-ai-transcription.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>When Your Phone&apos;s Mic Beats Your Expensive Gear</itunes:title>
      <itunes:subtitle>Stop holding your phone like a piece of toast. Explore the best mobile microphone setups for high-quality AI voice transcription.</itunes:subtitle>
      <itunes:summary><![CDATA[Why might your smartphone's internal microphone outperform dedicated external mics for AI transcription? We explore the surprising benchmarks, the role of proximity and processing, and how to choose gear that actually improves clarity for speech-to-text engines like Whisper.]]></itunes:summary>
      <itunes:duration>1865</itunes:duration>
      <itunes:episode>868</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/mobile-audio-ai-transcription.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/mobile-audio-ai-transcription.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Recorded Voice Sounds Wrong</title>
      <description><![CDATA[Why does your recorded voice sound nasal and unfamiliar? This episode explores the psychoacoustic gap between bone conduction and air conduction, and whether AI-driven EQ can bridge it—or just mask the problem.]]></description>
      <link>https://myweirdprompts.com/episode/ai-vocal-eq-mastering/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/ai-vocal-eq-mastering/</guid>
      <pubDate>Fri, 20 Feb 2026 16:24:01 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/ai-vocal-eq-mastering.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Why Your Recorded Voice Sounds Wrong</itunes:title>
      <itunes:subtitle>Use AI to find your perfect EQ profile and build a pro vocal chain. Fix nasality, master de-essing, and sound your best on any device.</itunes:subtitle>
      <itunes:summary><![CDATA[Why does your recorded voice sound nasal and unfamiliar? This episode explores the psychoacoustic gap between bone conduction and air conduction, and whether AI-driven EQ can bridge it—or just mask the problem.]]></itunes:summary>
      <itunes:duration>1843</itunes:duration>
      <itunes:episode>732</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/ai-vocal-eq-mastering.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/ai-vocal-eq-mastering.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Math of Immersion: How 360-Degree Sound Actually Works</title>
      <description><![CDATA[For decades, surround sound required a room full of wires and precisely placed speakers, but the digital age has changed the rules of acoustics. This episode explores the transition from channel-based audio to object-based systems like Dolby Atmos, explaining how software can now simulate a theater experience on a smartphone or a single soundbar. We dive into the physics of beamforming, the "magic" of Head Related Transfer Functions, and how AI-driven computational audio is mapping our living rooms in real-time to create a perfect soundstage. Whether you're an audiophile or just curious about that "spatial audio" toggle on your phone, this deep dive reveals the engineering behind the bubble of sound.]]></description>
      <link>https://myweirdprompts.com/episode/spatial-audio-evolution-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/spatial-audio-evolution-explained/</guid>
      <pubDate>Fri, 20 Feb 2026 15:23:35 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/spatial-audio-evolution-explained.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>The Math of Immersion: How 360-Degree Sound Actually Works</itunes:title>
      <itunes:subtitle>Learn how object-based audio and clever math trick your brain into hearing 360-degree sound from even the smallest mobile devices.</itunes:subtitle>
      <itunes:summary><![CDATA[For decades, surround sound required a room full of wires and precisely placed speakers, but the digital age has changed the rules of acoustics. This episode explores the transition from channel-based audio to object-based systems like Dolby Atmos, explaining how software can now simulate a theater experience on a smartphone or a single soundbar. We dive into the physics of beamforming, the "magic" of Head Related Transfer Functions, and how AI-driven computational audio is mapping our living rooms in real-time to create a perfect soundstage. Whether you're an audiophile or just curious about that "spatial audio" toggle on your phone, this deep dive reveals the engineering behind the bubble of sound.]]></itunes:summary>
      <itunes:duration>1709</itunes:duration>
      <itunes:episode>727</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/spatial-audio-evolution-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/spatial-audio-evolution-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Finding a Speaker That Loves Voices</title>
      <description><![CDATA[Most speakers are tuned for music's bass and treble, but podcast listeners need clarity in the mid-range. This episode explores the hardware and tuning that make voices sound intimate and consistent, even in a small apartment, and reviews top contenders like the Apple HomePod and Sonos Era 300.]]></description>
      <link>https://myweirdprompts.com/episode/podcast-speaker-vocal-clarity/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/podcast-speaker-vocal-clarity/</guid>
      <pubDate>Fri, 20 Feb 2026 14:35:10 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/podcast-speaker-vocal-clarity.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Finding a Speaker That Loves Voices</itunes:title>
      <itunes:subtitle>Stop listening to podcasts through tinny speakers. Learn how to choose hardware optimized for the human voice and clear, room-filling audio.</itunes:subtitle>
      <itunes:summary><![CDATA[Most speakers are tuned for music's bass and treble, but podcast listeners need clarity in the mid-range. This episode explores the hardware and tuning that make voices sound intimate and consistent, even in a small apartment, and reviews top contenders like the Apple HomePod and Sonos Era 300.]]></itunes:summary>
      <itunes:duration>1813</itunes:duration>
      <itunes:episode>725</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/podcast-speaker-vocal-clarity.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/podcast-speaker-vocal-clarity.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Ears Prefer Imperfect Plastic to Perfect Pixels</title>
      <description><![CDATA[In an era of 32-bit lossless streaming and neural-link audio, the humble vinyl record remains a juggernaut of the music industry, defying every technological logic of the mid-2020s. This episode dives into the technical reality behind "analog warmth," revealing why the format’s physical limitations actually protect the music from the modern "Loudness War" and digital compression. From the psychology of the "IKEA effect" to the surprising durability of polyvinyl chloride, we explore why the world refuses to let go of the needle and the groove. Discover why the most "imperfect" medium might actually be the most satisfying way to experience sound in a frictionless digital age.]]></description>
      <link>https://myweirdprompts.com/episode/vinyl-analog-audio-persistence/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/vinyl-analog-audio-persistence/</guid>
      <pubDate>Fri, 20 Feb 2026 09:22:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/vinyl-analog-audio-persistence.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Why Your Ears Prefer Imperfect Plastic to Perfect Pixels</itunes:title>
      <itunes:subtitle>Why do we still buy plastic discs in an age of neural-link streaming? Explore the science of analog warmth and the &quot;ritual&quot; of the record.</itunes:subtitle>
      <itunes:summary><![CDATA[In an era of 32-bit lossless streaming and neural-link audio, the humble vinyl record remains a juggernaut of the music industry, defying every technological logic of the mid-2020s. This episode dives into the technical reality behind "analog warmth," revealing why the format’s physical limitations actually protect the music from the modern "Loudness War" and digital compression. From the psychology of the "IKEA effect" to the surprising durability of polyvinyl chloride, we explore why the world refuses to let go of the needle and the groove. Discover why the most "imperfect" medium might actually be the most satisfying way to experience sound in a frictionless digital age.]]></itunes:summary>
      <itunes:duration>1610</itunes:duration>
      <itunes:episode>720</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/vinyl-analog-audio-persistence.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/vinyl-analog-audio-persistence.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Bit Rate Dilemma: How Much Audio Data Do You Need?</title>
      <description><![CDATA[In this technical yet practical episode, Herman and Corn respond to a challenge from their housemate Daniel regarding the "data-gluttony" of their podcast's high bit rate. They peel back the layers of digital audio compression, explaining how psychoacoustics allows encoders to "lie" to the human brain by stripping away redundant sounds. The discussion covers the crucial difference between mono and stereo bit rate allocation, revealing why a 192 kbps stereo file might be a "safety margin" rather than a necessity. Furthermore, they examine the surprising requirements of modern AI transcription tools and the specialized needs of forensic audio recording. By the end of the conversation, listeners will understand how to choose the right data budget for any scenario, from casual voice notes to high-fidelity archival masters.]]></description>
      <link>https://myweirdprompts.com/episode/audio-bitrate-compression-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/audio-bitrate-compression-explained/</guid>
      <pubDate>Tue, 17 Feb 2026 12:14:54 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/audio-bitrate-compression-explained.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>The Bit Rate Dilemma: How Much Audio Data Do You Need?</itunes:title>
      <itunes:subtitle>Herman and Corn explore the science of audio compression, psychoacoustics, and finding the perfect bit rate for podcasts and AI.</itunes:subtitle>
      <itunes:summary><![CDATA[In this technical yet practical episode, Herman and Corn respond to a challenge from their housemate Daniel regarding the "data-gluttony" of their podcast's high bit rate. They peel back the layers of digital audio compression, explaining how psychoacoustics allows encoders to "lie" to the human brain by stripping away redundant sounds. The discussion covers the crucial difference between mono and stereo bit rate allocation, revealing why a 192 kbps stereo file might be a "safety margin" rather than a necessity. Furthermore, they examine the surprising requirements of modern AI transcription tools and the specialized needs of forensic audio recording. By the end of the conversation, listeners will understand how to choose the right data budget for any scenario, from casual voice notes to high-fidelity archival masters.]]></itunes:summary>
      <itunes:duration>1674</itunes:duration>
      <itunes:episode>660</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/audio-bitrate-compression-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/audio-bitrate-compression-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Golden Rule of Audio Engineering</title>
      <description><![CDATA[Why do we ever leave the pristine world of digital? This episode explores the engineering philosophy that dictates keeping signals digital until the very last millisecond before they reach your ears.]]></description>
      <link>https://myweirdprompts.com/episode/digital-to-analog-audio-science/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/digital-to-analog-audio-science/</guid>
      <pubDate>Mon, 16 Feb 2026 19:56:24 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/digital-to-analog-audio-science.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>The Golden Rule of Audio Engineering</itunes:title>
      <itunes:subtitle>Why does digital data need to become analog? Explore the physics of sound and the critical role of the DAC in modern audio engineering.</itunes:subtitle>
      <itunes:summary><![CDATA[Why do we ever leave the pristine world of digital? This episode explores the engineering philosophy that dictates keeping signals digital until the very last millisecond before they reach your ears.]]></itunes:summary>
      <itunes:duration>1440</itunes:duration>
      <itunes:episode>647</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/digital-to-analog-audio-science.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/digital-to-analog-audio-science.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Audio Engineering as Prompt Engineering: Better Sound, Better AI</title>
      <description><![CDATA[In this episode of My Weird Prompts, Corn and Herman tackle a fascinating listener question from their housemate, Daniel: does the quality of your audio input actually change the way an AI responds? The duo explores the practical side of mobile production, highlighting essential Android tools like ASR and AudioLab, alongside the "gold standard" cloud service, Auphonic, for achieving professional results on the go. Beyond the gear, the conversation shifts into deep AI theory, examining how multimodal models like Gemini 3 process audio tokens. Herman explains how background noise and compression can "distract" a model's attention mechanism, potentially degrading its reasoning capabilities. By the end of this episode, you’ll understand why audio engineering is the next frontier of prompt engineering and how to optimize your voice recordings to get the most sophisticated responses from the latest LLMs.]]></description>
      <link>https://myweirdprompts.com/episode/audio-quality-ai-responses/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/audio-quality-ai-responses/</guid>
      <pubDate>Thu, 12 Feb 2026 11:34:34 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/audio-quality-ai-responses.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Audio Engineering as Prompt Engineering: Better Sound, Better AI</itunes:title>
      <itunes:subtitle>Can better audio quality actually make an AI smarter? Discover how audio post-production functions as a new form of prompt engineering.</itunes:subtitle>
      <itunes:summary><![CDATA[In this episode of My Weird Prompts, Corn and Herman tackle a fascinating listener question from their housemate, Daniel: does the quality of your audio input actually change the way an AI responds? The duo explores the practical side of mobile production, highlighting essential Android tools like ASR and AudioLab, alongside the "gold standard" cloud service, Auphonic, for achieving professional results on the go. Beyond the gear, the conversation shifts into deep AI theory, examining how multimodal models like Gemini 3 process audio tokens. Herman explains how background noise and compression can "distract" a model's attention mechanism, potentially degrading its reasoning capabilities. By the end of this episode, you’ll understand why audio engineering is the next frontier of prompt engineering and how to optimize your voice recordings to get the most sophisticated responses from the latest LLMs.]]></itunes:summary>
      <itunes:duration>1323</itunes:duration>
      <itunes:episode>598</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/audio-quality-ai-responses.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/audio-quality-ai-responses.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>How Math Gives Microphones Directional Ears</title>
      <description><![CDATA[How do tiny conference speakers pick out your voice in a noisy room? This episode unpacks the physics and digital signal processing behind beamforming—using time delays and wave interference to make microphones 'look' where they listen.]]></description>
      <link>https://myweirdprompts.com/episode/beamforming-audio-technology-explained/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/beamforming-audio-technology-explained/</guid>
      <pubDate>Thu, 15 Jan 2026 22:44:51 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/beamforming-audio-technology-explained.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>How Math Gives Microphones Directional Ears</itunes:title>
      <itunes:subtitle>Discover how math and physics turn simple microphones into &quot;sound spotlights&quot; that can isolate a single voice in even the noisiest environments.</itunes:subtitle>
      <itunes:summary><![CDATA[How do tiny conference speakers pick out your voice in a noisy room? This episode unpacks the physics and digital signal processing behind beamforming—using time delays and wave interference to make microphones 'look' where they listen.]]></itunes:summary>
      <itunes:duration>1368</itunes:duration>
      <itunes:episode>233</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/beamforming-audio-technology-explained.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/beamforming-audio-technology-explained.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Why Your Irish Accent Sounds American</title>
      <description><![CDATA[Herman and Corn explore why modern voice cloning systems default to American cadences for regional accents, and how transformer-based models are finally learning to capture authentic prosody and emotion.]]></description>
      <link>https://myweirdprompts.com/episode/voice-cloning-neural-tts/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/voice-cloning-neural-tts/</guid>
      <pubDate>Thu, 08 Jan 2026 13:49:44 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/voice-cloning-neural-tts.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Why Your Irish Accent Sounds American</itunes:title>
      <itunes:subtitle>Herman and Corn dive into the mechanics of neural text-to-speech, exploring how AI masters human prosody and the &quot;average voice&quot; accent problem.</itunes:subtitle>
      <itunes:summary><![CDATA[Herman and Corn explore why modern voice cloning systems default to American cadences for regional accents, and how transformer-based models are finally learning to capture authentic prosody and emotion.]]></itunes:summary>
      <itunes:duration>1408</itunes:duration>
      <itunes:episode>196</itunes:episode>
      <itunes:season>2</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/voice-cloning-neural-tts.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/voice-cloning-neural-tts.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>The Mic That Hears You from Across the Desk</title>
      <description><![CDATA[Why consumer microphones fail at voice dictation and how professional boundary mics can deliver 99% accuracy without a headset. Herman and Corn explore the audio engineering behind hands-free computing.]]></description>
      <link>https://myweirdprompts.com/episode/voice-dictation-microphone-guide/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/voice-dictation-microphone-guide/</guid>
      <pubDate>Wed, 24 Dec 2025 15:15:40 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/voice-dictation-microphone-guide.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>The Mic That Hears You from Across the Desk</itunes:title>
      <itunes:subtitle>Tired of headsets? Herman and Corn explore professional microphone setups for seamless, high-accuracy AI voice dictation from a distance.</itunes:subtitle>
      <itunes:summary><![CDATA[Why consumer microphones fail at voice dictation and how professional boundary mics can deliver 99% accuracy without a headset. Herman and Corn explore the audio engineering behind hands-free computing.]]></itunes:summary>
      <itunes:duration>1456</itunes:duration>
      <itunes:episode>99</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/voice-dictation-microphone-guide.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/voice-dictation-microphone-guide.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>Clean Audio, Messy Reality: Noise Removal for Voice-to-Text</title>
      <description><![CDATA[When you need to record a voice memo while holding a fussy baby, which noise removal strategy actually works? Herman and Corn dive deep into the trade-offs between real-time on-device processing, cloud-based post-processing, and hardware microphone solutions. Discover why audio that sounds cleaner to human ears might actually transcribe worse, and learn which approach makes sense for your workflow. A practical guide to the neural networks and signal processing powering modern voice recording technology.]]></description>
      <link>https://myweirdprompts.com/episode/clean-audio-messy-reality-noise-removal-for-voice-to-text/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/clean-audio-messy-reality-noise-removal-for-voice-to-text/</guid>
      <pubDate>Fri, 12 Dec 2025 04:34:48 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/clean-audio-messy-reality-noise-removal-for-voice-to-text.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>Clean Audio, Messy Reality: Noise Removal for Voice-to-Text</itunes:title>
      <itunes:subtitle>Fussy baby, clean audio? We dive into noise removal for voice-to-text. Discover why cleaner audio can transcribe worse.</itunes:subtitle>
      <itunes:summary><![CDATA[When you need to record a voice memo while holding a fussy baby, which noise removal strategy actually works? Herman and Corn dive deep into the trade-offs between real-time on-device processing, cloud-based post-processing, and hardware microphone solutions. Discover why audio that sounds cleaner to human ears might actually transcribe worse, and learn which approach makes sense for your workflow. A practical guide to the neural networks and signal processing powering modern voice recording technology.]]></itunes:summary>
      <itunes:duration>1715</itunes:duration>
      <itunes:episode>58</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/clean-audio-messy-reality-noise-removal-for-voice-to-text.png"/>
      <itunes:explicit>no</itunes:explicit>
      <podcast:transcript url="https://episodes.myweirdprompts.com/transcripts/clean-audio-messy-reality-noise-removal-for-voice-to-text.md" type="text/plain" language="en"/>
    </item>

    <item>
      <title>From Lawyers in Limousines to Developers in Their PJs: The Voice Tech Revolution</title>
      <description><![CDATA[Who actually uses voice technology in 2024 and beyond? Herman and Corn explore how OpenAI's Whisper has transformed voice dictation from a niche professional tool into a mainstream productivity revolution. They discuss the expanding user base, the disconnect between cutting-edge products and outdated marketing, accessibility benefits, and why voice tech is becoming a genuine 'force for good' for neurodivergent users and creative professionals alike.]]></description>
      <link>https://myweirdprompts.com/episode/the-evolving-voice-tech-user-base/</link>
      <guid isPermaLink="false">https://myweirdprompts.com/episode/the-evolving-voice-tech-user-base/</guid>
      <pubDate>Thu, 11 Dec 2025 14:17:18 GMT</pubDate>
      <enclosure
        url="https://dts.podtrac.com/redirect.m4a/episodes.myweirdprompts.com/audio/the-evolving-voice-tech-user-base.m4a"
        type="audio/mp4"
        length="0"
      />
      <itunes:title>From Lawyers in Limousines to Developers in Their PJs: The Voice Tech Revolution</itunes:title>
      <itunes:subtitle>From limo-riding lawyers to pajama-clad coders, voice tech is booming. Discover how AI is making it a force for good.</itunes:subtitle>
      <itunes:summary><![CDATA[Who actually uses voice technology in 2024 and beyond? Herman and Corn explore how OpenAI's Whisper has transformed voice dictation from a niche professional tool into a mainstream productivity revolution. They discuss the expanding user base, the disconnect between cutting-edge products and outdated marketing, accessibility benefits, and why voice tech is becoming a genuine 'force for good' for neurodivergent users and creative professionals alike.]]></itunes:summary>
      <itunes:duration>1790</itunes:duration>
      <itunes:episode>57</itunes:episode>
      <itunes:season>1</itunes:season>
      <itunes:image href="https://files.myweirdprompts.com/covers/the-evolving-voice-tech-user-base.png"/>
      <itunes:explicit>no</itunes:explicit>
      
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