# HEARSAY-IV: Speech Transcription You Can Argue With | Meta Hackathon 2026

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Mike Kuykendall](https://cerebralvalley.ai/u/MikeKuykendall)
- **GitHub:** https://github.com/Michael-A-Kuykendall/hearsay-four
- **Demo video:** https://www.youtube.com/watch?v=cwq41UllDHU
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3834

HEARSAY-IV — Speech Transcription You Can Argue With.

What if a speech recognition system could explain exactly why it chose a word, show the alternatives it rejected, and undo a correction without losing its original state?

HEARSAY-IV is an experimental, non-neural speech transcription system built for the Meta Global AI Developer Hackathon 2026.

Inspired by the original HEARSAY-II blackboard architecture, it combines classical speech recognition with a Saturation Fabric that makes word decisions inspectable.

**What the demo shows:**
- Real speech transcription with a word-by-word explanation panel.
- A classical MFCC/HMM acoustic model, Kneser-Ney trigram language model, and 259,000-word lexicon.
- Near-linear scaling: measured growth exponent improved from 3.84 to 1.07.
- Processing up to 67,000 competing hypotheses per sentence, faster than real time.
- Reversible corrections recorded as facts, with byte-identical undo.
- A pre-registered experiment testing whether retaining dormant hypotheses improves recognition compared with pruning.

**The experimental results are reported without cherry-picking.**

Dormancy did not outperform pruning. On the evaluated test set, the classical decoder had a 64.3% word error rate, while the Fabric had an 85.5% word error rate. The experiment established a scaling improvement and an inspectable decision mechanism, not an accuracy breakthrough.

One correction repaired 7 of 16 single-word errors in a separate test.

Built using Muse Code with parallel development agents, verifiable tasks, independent review, and human-controlled acceptance decisions.

Source code:
https://github.com/Michael-A-Kuykendall/hearsay-four
(Repository will become public after release checks.)

LibriSpeech audio dataset: OpenSLR 12, CC BY 4.0.

#HearsayIV #SpeechRecognition #MetaHackathon

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