通过FST重写语音识别词元图，提升罕见词识别准确率
Spelling Correction through Rewriting of Non-Autoregressive ASR Lattices
- 用FST直接从词元转换音素，跳过显式词语表示
- 在含上下文相关实体的测试集上，句错误率降低15.2%
- 无需重训练模型，适用于已有CTC语音识别系统
端到端自动语音识别（ASR）模型在识别个人或罕见短语时表现不佳。一种有前景的改进方法是通过拼写纠错（或重写）对ASR词元图进行修正，将可能误识别的短语替换为声学相似且上下文相关的替代词。然而，由于基于连接时序分类（CTC）的非自回归、上下文无关的束搜索会产生噪声假设，这使得重写极具挑战性。本文提出一种针对基于Transformer的CTC模型生成的词元图的有限状态转换器（FST）重写技术。该算法直接从词元执行字符到音素（G2P）转换，避免了显式词语表示，并充分利用了CTC词元图的丰富信息。该方法无需重新训练或修改原有ASR模型。在包含上下文相关实体的测试集上，实现了最高达15.2%的相对句错误率（SER）降低。
原文摘要 · Abstract (English)
For end-to-end Automatic Speech Recognition (ASR) models, recognizing personal or rare phrases can be hard. A promising way to improve accuracy is through spelling correction (or rewriting) of the ASR lattice, where potentially misrecognized phrases are replaced with acoustically similar and contextually relevant alternatives. However, rewriting is challenging for ASR models trained with connectionist temporal classification (CTC) due to noisy hypotheses produced by a non-autoregressive, context-independent beam search. We present a finite-state transducer (FST) technique for rewriting wordpiece lattices generated by Transformer-based CTC models. Our algorithm performs grapheme-to-phoneme (G2P) conversion directly from wordpieces into phonemes, avoiding explicit word representations and exploiting the richness of the CTC lattice. Our approach requires no retraining or modification of the ASR model. We achieved up to a 15.2% relative reduction in sentence error rate (SER) on a test set with contextually relevant entities.
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