用注意力模型提升语音识别中候选词的评分与过滤效果。
A Neural Model for Contextual Biasing Score Learning and Filtering
- 基于注意力机制的解码器，根据声学特征为候选词打分。
- 在Librispeech数据集上过滤掉多数无关候选词，提升识别准确率。
- 模块化设计，可适配任意ASR系统，适合需要外部知识融合的场景。
上下文偏置通过在解码过程中融入用户特定短语或实体等外部知识，提升自动语音识别（ASR）性能。本文提出一种基于注意力的偏置解码器，利用ASR编码器提取的声学信息为候选短语生成得分，可用于剔除低概率短语，并计算浅融合偏置的奖励值。我们引入每标记的判别性目标函数，促使真实短语得分更高，同时抑制干扰项。在Librispeech偏置基准上的实验表明，该方法能有效过滤绝大多数候选短语，并在不同偏置条件下显著提升浅融合偏置的识别准确率。本方法具有模块化特性，可与任意ASR系统结合，其过滤机制还可提升其他偏置方法的性能。
原文摘要 · Abstract (English)
Contextual biasing improves automatic speech recognition (ASR) by integrating external knowledge, such as user-specific phrases or entities, during decoding. In this work, we use an attention-based biasing decoder to produce scores for candidate phrases based on acoustic information extracted by an ASR encoder, which can be used to filter out unlikely phrases and to calculate bonus for shallow-fusion biasing. We introduce a per-token discriminative objective that encourages higher scores for ground-truth phrases while suppressing distractors. Experiments on the Librispeech biasing benchmark show that our method effectively filters out majority of the candidate phrases, and significantly improves recognition accuracy under different biasing conditions when the scores are used in shallow fusion biasing. Our approach is modular and can be used with any ASR system, and the filtering mechanism can potentially boost performance of other biasing methods.
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