Voice Memory for Agentic Speech Recognition
Quick Answer
Voice Memory introduces an inference-only scheme for agentic speech recognition, reducing generative error correction over-corrections from 64% to 35%.
Quick Take
It lowers the weighted word error rate from 8.36% to 7.52% across ten HyPoradise domains, significantly improving performance in areas like air-travel commands. The system operates without changing weights and adds no parameters to the inference path.
Key Points
- Voice Memory reduces generative error correction over-corrections from 64% to 35%.
- Weighted word error rate drops from 8.36% to 7.52% across ten domains.
- Performance gains are notable in air-travel commands, improving from 8.40% to 3.40%.
- The system maintains auditability and portability without changing model weights.
- Demo and example code are available for further research.
Paper Resources
Source Excerpt
We present Voice Memory, a inference-only scheme for agentic speech recognition: at stream time, a frozen corrector reads a single per-domain memory. md and decides per utterance whether to act on the hypothesis or abstain and keep the 1-best. Asynchronously, a score-gated optimizer revises that file through bounded edits, accepting an edit only when it strictly improves a held-out score. Extended from classical ASR-LM framework, we refer this split the listener-thinker architecture; the two role
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