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End-to-end automatic speech recognition (E2E-ASR) has the potential toimprove performance, but a specific issue that needs to be addressed is thedifficulty it has in handling enharmonic words: named entities (NEs) with thesame pronunciation and part of speech that are spelled differently. This oftenoccurs with Japanese personal names that have the same pronunciation butdifferent 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-ASRsbased on a named-entity-aware E2E-ASR model and phoneme similarity estimation.Experimental results show that the proposed method improves the target NEcharacter error rate by 35.7% on average relative to the conventional E2E-ASRmodel when selecting personal names as a target NE.

Retraining-free Customized ASR for Enharmonic Words Based on a Named-Entity-Aware Model and Phoneme Similarity Estimation