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Incorporating Class-based Language Model for Named Entity Recognition in Factorized Neural Transducer

arXiv 2309.07648 ↗First posted 14 Sep 2023eess.AS cs.CL cs.SDcited by 0

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Abstract (arXiv)

Despite advancements of end-to-end (E2E) models in speech recognition, named entity recognition (NER) is still challenging but critical for semantic understanding. Previous studies mainly focus on various rule-based or attention-based contextual biasing algorithms. However, their performance might be sensitive to the biasing weight or degraded by excessive attention to the named entity list, along with a risk of false triggering. Inspired by the success of the class-based language model (LM) in NER in conventional hybrid systems and the effective decoupling of acoustic and linguistic information in the factorized neural Transducer (FNT), we propose C-FNT, a novel E2E model that incorporates class-based LMs into FNT. In C-FNT, the LM score of named entities can be associated with the name class instead of its surface form. The experimental results show that our proposed C-FNT significantly reduces error in named entities without hurting performance in general word recognition.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 34 references that are in the map. First posted on arXiv 14 Sep 2023.

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References · 26

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14 more
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