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Incorporating Class-based Language Model for Named Entity Recognition in Factorized Neural Transducer
- Publication Year :
- 2023
-
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.<br />Comment: Accepted in INTERSPEECH 2024
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2309.07648
- Document Type :
- Working Paper