改进语音识别端点检测，减少延迟与误判，提升对话体验
Improving endpoint detection in end-to-end streaming ASR for conversational speech
- 引入词尾标记与延迟惩罚，缓解模型输出延迟问题
- 通过辅助网络实现帧级语音活动检测，提升端点判断准确性
- 适用于实时对话系统，尤其适合流式语音识别场景
在支持人机或人际对话的智能产品中，端点检测（EP）对用户体验至关重要。基于转换器的端到端语音识别（T-ASR）虽适用于流式处理，但存在输出延迟问题，易导致端点检测错误或延迟。这会引发用户被过早打断或感知延迟增加，影响交互体验。本文提出新方法：在每个词后添加词尾标记，并引入延迟惩罚机制以缓解输出延迟；同时使用辅助网络实现可靠的帧级语音活动检测，提升端点判断精度。实验在Switchboard对话语料库上进行，相较基线延迟惩罚方法，显著改善了端点检测性能。
原文摘要 · Abstract (English)
ASR endpointing (EP) plays a major role in delivering a good user experience in products supporting human or artificial agents in human-human/machine conversations. Transducer-based ASR (T-ASR) is an end-to-end (E2E) ASR modelling technique preferred for streaming. A major limitation of T-ASR is delayed emission of ASR outputs, which could lead to errors or delays in EP. Inaccurate EP will cut the user off while speaking, returning incomplete transcript while delays in EP will increase the perceived latency, degrading the user experience. We propose methods to improve EP by addressing delayed emission along with EP mistakes. To address the delayed emission problem, we introduce an end-of-word token at the end of each word, along with a delay penalty. The EP delay is addressed by obtaining a reliable frame-level speech activity detection using an auxiliary network. We apply the proposed methods on Switchboard conversational speech corpus and evaluate it against a delay penalty method.
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