让语音助手学会从对话中持续改进，提升识别准确率。
An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems
- 用师生学习+上下文感知，让语音识别随对话动态优化。
- 真实对话系统相对错误率降低近10%，合成数据最高降26%。
- 适合做智能语音助手、多轮对话系统的研发人员参考。
语音助手等对话系统需应对复杂多变的交互场景。传统自动语音识别（ASR）系统通常独立处理每一轮对话，无法适应上下文或吸收用户反馈。本文提出一种通用框架，使ASR不仅能基于单轮语音训练，还能在多轮对话中持续学习显式标注与隐式用户反馈。通过结合师生学习与上下文感知对话处理，并设计名为Ohm的新在线难例挖掘方法，实现对比自监督学习。实验表明，相比传统训练方式，该框架在真实对话系统中将相对词错误率（WER）降低近10%，在公开合成数据上最高降低26%。
原文摘要 · Abstract (English)
Dialog systems, such as voice assistants, are expected to engage with users in complex, evolving conversations. Unfortunately, traditional automatic speech recognition (ASR) systems deployed in such applications are usually trained to recognize each turn independently and lack the ability to adapt to the conversational context or incorporate user feedback. In this work, we introduce a general framework for ASR in dialog systems that can go beyond learning from single-turn utterances and learn over time how to adapt to both explicit supervision and implicit user feedback present in multi-turn conversations. We accomplish that by leveraging advances in student-teacher learning and context-aware dialog processing, and designing contrastive self-supervision approaches with Ohm, a new online hard-negative mining approach. We show that leveraging our new framework compared to traditional training leads to relative WER reductions of close to 10% in real-world dialog systems, and up to 26% on public synthetic data.
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