提升罕见词识别准确率，通过深层上下文建模优化语音识别
Deep CLAS: Deep Contextual Listen, Attend and Spell
- 用字符级编码和Conformer替代短语级编码，捕捉更细粒度上下文
- 引入偏置损失与增强查询的注意力机制，显著提升命名实体识别性能
- 适用于需要高精度罕见词识别的语音识别场景
上下文感知的听、关注、拼写（CLAS）已被证明能有效提升罕见词的自动语音识别（ASR）性能。然而，其依赖短语级上下文建模与基于注意力的相关性评分，缺乏显式上下文约束，导致上下文信息利用不足。本文提出深度上下文感知的听、关注、拼写（Deep CLAS），以更好利用上下文信息。引入偏置损失促使模型关注上下文；丰富偏置注意力的查询以提高评分准确性；采用字符级编码代替短语级编码，并使用Conformer而非LSTM进行上下文编码，实现细粒度上下文建模；直接用偏置注意力得分修正模型输出概率分布。在公开数据集AISHELL-1和AISHELL-NER上的实验表明，在命名实体识别场景中，相比基线CLAS，Deep CLAS相对召回率提升65.78%，相对F1分数提升53.49%。
原文摘要 · Abstract (English)
Contextual-LAS (CLAS) has been shown effective in improving Automatic Speech Recognition (ASR) of rare words. It relies on phrase-level contextual modeling and attention-based relevance scoring without explicit contextual constraint which lead to insufficient use of contextual information. In this work, we propose deep CLAS to use contextual information better. We introduce bias loss forcing model to focus on contextual information. The query of bias attention is also enriched to improve the accuracy of the bias attention score. To get fine-grained contextual information, we replace phrase-level encoding with character-level encoding and encode contextual information with conformer rather than LSTM. Moreover, we directly use the bias attention score to correct the output probability distribution of the model. Experiments using the public AISHELL-1 and AISHELL-NER. On AISHELL-1, compared to CLAS baselines, deep CLAS obtains a 65.78% relative recall and a 53.49% relative F1-score increase in the named entity recognition scene.
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