Ask, Don't Guess: Targeted Clarification for Speech Recognition Errors
Speech recognition in real time in far-field conversational settings still incurs significant errors.
ProofPaper ↗
Key points
- Identifying potential error spans during streaming ASR permits a recourse mechanism where the speaker can be asked to repeat the affected content under cleaner acoustic conditions.
- We propose \sysname, a new framework for online error localization, that identifies spans likely to benefit from this intervention.
- Our supervision uses clean audio to estimate how the recognizer's output might change under improved acoustic conditions.
- We probabilistically align noisy and clean decodes using a pair-HMM to obtain soft supervision.
Sources (1)
- [1]Ask, Don't Guess: Targeted Clarification for Speech Recognition ErrorsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 06:32 PM
Speech recognition in real time in far-field conversational settings still incurs significant errors.
Identifying potential error spans during streaming ASR permits a recourse mechanism where the speaker can be asked to repeat the affected content under cleaner acoustic conditions.
Extractive summary: sentences quoted from the sources.
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