#automatic-speech-recognition
14 episodes
#5466: Hebrew Words Hidden in English Text
Daniel wants a classifier that spots Hebrew written in Latin letters — and it turns out nobody's built one.
#5435: When Your TTS Model Eats the Numbers
Numbers, dates, and acronyms break text-to-speech in specific, documented ways. Here's where normalization lives — and why it depends on your model.
#5431: The Other Half of Hugging Face: Why BERT Still Out-Downloads Llama
Encoder models pull over a billion downloads a month. Decoder models pull 397 million. The AI conversation and the download counter are describing ...
#5430: Two Boxes: ASR and the Text Fixer Behind It
Punctuation, casing, ITN, disfluency — the four-job layer between raw ASR output and text you can actually read.
#5397: Chaining Small Models for Voice Cleanup
Six cleanup stages at 97% accuracy each compound to 83% end-to-end. So how many small models can you actually chain?
#4958: Multi-Model Transcription with LLM Reconciliation
Using multiple transcription engines and an LLM judge to catch errors in voice-to-text — especially tricky technical terms.
#4919: Parakeet vs Whisper: On-Device Dictation Showdown
NVIDIA Parakeet beats Whisper on phone dictation despite having more parameters. Here's why architecture matters more than model size.
#4312: Why Speech-to-Text Still Fails at Its Own Name
When OpenAI's Whisper misheard its own name as "Wispr," it revealed why 95% word accuracy still isn't good enough.
#2754: Why Your Dictation Setup Might Be Wrong
Modern ASR is shockingly robust. The biggest predictor of accuracy? How well your audio matches its training data.
#2590: The Uncanny Valley of Clean Speech
How transformer models distinguish "um" from meaningful speech — and why removing too much makes you sound like a robot.
#2486: Why Noise Reduction Can Ruin Transcription Accuracy
Cleaning audio before transcription can increase errors by up to 46%. Here's the right approach for your voice app.
#2337: When Diarization Fails Silently
Discover how PyAnnote and other tools tackle the critical task of identifying "who spoke when" in audio—and why it’s harder than it sounds.
#109: Beating Context Bloat with Dynamic Dictionaries
Tired of AI mishearing brand names? Learn how to build efficient custom dictionaries for Gemini 1.5 without breaking the bank.
#10: How ASR Went From Frustration To ... Whisper Magic
Speech to text: from frustrating to fantastic. Uncover the magic behind its rapid rise and connection to the AI boom!