DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition
CoRR(2024)
摘要
End-to-end automatic speech recognition (E2E ASR) systems often suffer from
mistranscription of domain-specific phrases, such as named entities, sometimes
leading to catastrophic failures in downstream tasks. A family of fast and
lightweight named entity correction (NEC) models for ASR have recently been
proposed, which normally build on phonetic-level edit distance algorithms and
have shown impressive NEC performance. However, as the named entity (NE) list
grows, the problems of phonetic confusion in the NE list are exacerbated; for
example, homophone ambiguities increase substantially. In view of this, we
proposed a novel Description Augmented Named entity CorrEctoR (dubbed DANCER),
which leverages entity descriptions to provide additional information to
facilitate mitigation of phonetic confusion for NEC on ASR transcription. To
this end, an efficient entity description augmented masked language model
(EDA-MLM) comprised of a dense retrieval model is introduced, enabling MLM to
adapt swiftly to domain-specific entities for the NEC task. A series of
experiments conducted on the AISHELL-1 and Homophone datasets confirm the
effectiveness of our modeling approach. DANCER outperforms a strong baseline,
the phonetic edit-distance-based NEC model (PED-NEC), by a character error rate
(CER) reduction of about 7
notably, when tested on Homophone that contain named entities of high phonetic
confusion, DANCER offers a more pronounced CER reduction of 46
PED-NEC for named entities.
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