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Project notebook / Audio ML

Implemented

Speech-to-Text Robustness & ASR Evaluation

I built a transcript-evaluation pipeline that aligns hypotheses to reference speech, separates substitution/deletion/insertion errors and reports corpus-weighted WER across noise, telephony and code-switching slices.

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What I built

  • Implemented Unicode-normalized word and character error rates with dynamic-programming edit alignment.
  • Built slice-aware corpus aggregation with separate error counts for clean, noisy, telephony and code-switching examples.
  • Built a JSONL evaluation CLI and deterministic test fixtures, so an actual recognizer adapter can be compared without changing the scoring contract.

Experiments & checks

  • The included four-pair illustrative corpus scores 4 word errors across 27 reference words (WER 14.8%). Those hypotheses are authored fixtures, not output from a trained recognizer. The scoring algorithm is implemented and unit tested; plugging in an ASR model is a separate integration.

Notes & limitations

A single WER number can hide where recognition breaks. A caller reading an account number through a narrowband phone connection has a different failure profile from a quiet-room dictation sample.

This implementation evaluates transcripts; it does not train, fine-tune or serve an ASR model. Actual accent, noise, code-switching and language performance require representative labeled recordings and speaker-separated test sets.

A model can understand the sentence and still get the account number wrong. The database tends to care about the number.

Screenshots & media

Screenshot of the public ZombieMutex page for Speech-to-Text Robustness & ASR Evaluation
Preview of the public project page or browser lab. This is not a screenshot of a private customer system. View full size ↗

Project updates

Dated notes tied to actual revisions of the site, not a simulated activity feed.

  1. Audio experiments published

    Added five implemented audio-ML projects with local experiments and measurement limits.

    Site revision 8d75d02

These entries reflect published changes to this website. The website repository is private; revision IDs are not public source links.

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