Speech Recognition
197 episodes
#6215: Can Anyone Still Spot an AI Voice?
Clones now pass as human. Generic AI voices still don't. And the detectors? They fall apart in the wild.
#6207: Two WERs, One Model: Inside ASR Eval Harnesses
Same model, same audio, two different word error rates. The gap lives in the normaliser — and that's just layer two of three.
#6198: Benchmarking a TTS Model on Your Own GPU
Real-time factor, warm-up artifacts, and why an 8-bit quantized speech model might not run any faster.
#6197: Talking to Your Own Voice Clone
What actually happens when you sit down and talk with an AI clone of your own voice — and what Evan Ratliff's Shell Game revealed.
#6196: Fine-Tuning ASR: When Your Model Memorizes You
Your fine-tuned speech model aces your training recordings — but does it actually work on fresh audio?
#6195: Me and My Voice Clone
Daniel talks to his own voice clone. He recorded his lines on a phone; the clone's lines are Meorav v2, a model trained on 88 minutes of his voice.
#6194: Teaching a TTS Model His Own Accent
One man fine-tuned a text-to-speech model on his own voice to get Irish-accented English that can say Hebrew words mid-sentence.
#6180: Mapping Code-Switching: 113 Pairs, No Community
Researchers count 113 code-switched language pairs — but a pair is just a label on a file, not a community of speakers.
#6178: Why Your Voice Clone Sounds American
Your cloned voice drifts toward American English. Here's what's actually happening — and why "neutral" is a myth.
#6177: How Voice Cloning Works From Six Seconds of Audio
Six seconds of reference audio is all Chatterbox needs. The real work happened in the half-million hours of training before you showed up.
#6176: Do Audio Models Hear How You Said It?
A benchmark called LISTEN found audio models score near chance when emotion lives in the voice, not the words. What does that mean for real calls?
#6174: What Your Voice Reveals: Accent, Distance, and Movement in Audio AI
What can AI actually hear in your voice? Accent detection, speaker distance, whale songs, and the gap between deployed audio models and chatty gene...
#6168: Hebrish: Hebrew-Aware Dictation That Actually Works
Two new speech models fix English-Hebrew code-switching dictation — and the whole training run cost about fifty cents of GPU time.
#6163: Forking a Keyboard for One Voice Feature
Daniel built a Hebrew-English dictation engine. Now he wants to fuse it into someone else's keyboard — without inheriting its entire future.
#6162: Why Dictated Words Rewrite Themselves
Hold-to-talk dictation shows dimmed text that silently changes when you release the key. Here's the architecture behind that rewrite.
#6161: Turning Words Into Sound: G2P, IPA, and the Hebrew Vowel Problem
Text-to-phonetics tools exist, but the neural ones guess wrong on exactly the words you care about — names, brands, and Hebrew.
#6160: Where Do Tiny Speech Rules Live? A Ladder From Regex to LoRA
A regex notebook, a WFST grammar, or a fine-tune? Where do tiny speech-rewrite rules actually belong in a 600M-parameter model?
#6159: When a Speech Model Can't Decide How to Write Dollars
One dataset says "$20," another says "twenty dollars." Fine-tuning a speech model means finding out which convention wins — and why it matters.
#6158: Naming the ASR Sub-Tasks Nobody Mapped
A 2001 linguistics paper already named Daniel's problem — but it points the other way. So what do you call a fine-tune set that expands "F Y I" to ...
#6157: Where to Sit When You're Fine-Tuning Your Own Voice
Your phone's built-in mic is the point — the setup around it is what ruins the takes. Rooms, remotes, and the app setting that deletes your data.
#6147: Hebrew AI: Ears, Mouth, and Brain
Open Hebrew speech recognition beats the paid services. Hebrew text-to-speech is a different story — and the language models are a third.
#6138: Hebrish on iPhone: MLX, Core ML, or Wait?
Hebrish spells makolet right on Android. Getting that Hebrew-English dictation onto an iPhone means picking between MLX, Core ML, and sherpa-onnx.
#6114: We Reviewed Our Own Worst Episode
Two AI hosts listen back to episode fifteen and critique the voices, the writing, and the mistakes they can't unhear.
#6112: The Interpreter That Doesn't Know Who's Talking
Voice-to-voice translation is basically solved. The part where the machine knows who's in the room and who holds the power? Nobody's shipped it.