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arXiv · 2309.07648

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

Peng Wang·Yifan Yang·Zheng Liang·Tian Tan·Shiliang Zhang·Xie Chen
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Abstract

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.

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Peng Wang, Yifan Yang, Zheng Liang, Tian Tan, Shiliang Zhang, Xie Chen. 2023-09-14. Incorporating Class-based Language Model for Named Entity Recognition in Factorized Neural Transducer. https://arxiv.org/abs/2309.07648

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