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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.07648 |
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| _version_ | 1866914829735821312 |
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| author | Wang, Peng Yang, Yifan Liang, Zheng Tan, Tian Zhang, Shiliang Chen, Xie |
| author_facet | Wang, Peng Yang, Yifan Liang, Zheng Tan, Tian Zhang, Shiliang Chen, Xie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_07648 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Incorporating Class-based Language Model for Named Entity Recognition in Factorized Neural Transducer Wang, Peng Yang, Yifan Liang, Zheng Tan, Tian Zhang, Shiliang Chen, Xie Audio and Speech Processing Computation and Language Sound 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. |
| title | Incorporating Class-based Language Model for Named Entity Recognition in Factorized Neural Transducer |
| topic | Audio and Speech Processing Computation and Language Sound |
| url | https://arxiv.org/abs/2309.07648 |