End-to-end automatic speech recognition (E2E-ASR) has the potential to
improve performance, but a specific issue that needs to be addressed is the
difficulty it has in handling enharmonic words: named entities (NEs) with the
same pronunciation and part of speech that are spelled differently. This often
occurs with Japanese personal names that have the same pronunciation but
different Kanji characters. Since such NE words tend to be important keywords,
ASR easily loses user trust if it misrecognizes them. To solve these problems,
this paper proposes a novel retraining-free customized method for E2E-ASRs
based on a named-entity-aware E2E-ASR model and phoneme similarity estimation.
Experimental results show that the proposed method improves the target NE
character error rate by 35.7% on average relative to the conventional E2E-ASR
model when selecting personal names as a target NE.

本文提出了一种基于实体感知的端到端自动语音识别模型和音素相似度估计的重新训练自由式定制方法，用于解决处理同音字的特定问题，实验结果表明，相对于传统的 E2E-ASR 模型，该方法在处理个人姓名作为目标实体时，平均改善了目标实体字符误差率 35.7%。