Teaching Speech Models to Choose Their Own Audio

Human transcription takes over eight hours of manual work for every hour of recorded audio. Active learning promises to cut that burden by letting speech models select which unlabelled recordings are most informative to transcribe next. Yet simple uncertainty measures often select garbage noise rather than useful speech, and sophisticated loss predictions frequently struggle against random baselines. Model disagreement committees, Monte Carlo dropout, and acoustic diversity clustering offer practical paths toward actual transcription savings. Key sources: • ISCA Archive, Long, Ye, Li & Liang, Active Learning for LF-MMI Trained Neural Networks in ASR, Interspeech 2018, pp. 2898 — https://www.isca-archive.org/interspeech_2018/long18_interspeech.pdf • Kundacina, Vincan & Miskovic, Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition, arXiv v2 revised 2025-04-25, published IEEE TASLP vol. 33, pp. 1862 — https://arxiv.org/abs/2406.02566v2 • Pokel, Zhao, Moure et al., Demonstration of Adapt4Me: An Uncertainty-Aware Authoring Environment for Personalizing Automatic Speech Recognition to Non-normative Speech, submitted 2026-03-20; retrieved 2026-11-08. — https://arxiv.org/abs/2603.20112v1 Episode 6495 — myweirdprompts.com/n/6495 (permanent short link: myweirdprompts.com/077976) Listen on Android: MWP FM Player — myweirdprompts.com/apk