Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916283016019968 |
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| author | Andrusenko, Andrei Laptev, Aleksandr Bataev, Vladimir Lavrukhin, Vitaly Ginsburg, Boris |
| author_facet | Andrusenko, Andrei Laptev, Aleksandr Bataev, Vladimir Lavrukhin, Vitaly Ginsburg, Boris |
| contents | Accurate recognition of rare and new words remains a pressing problem for contextualized Automatic Speech Recognition (ASR) systems. Most context-biasing methods involve modification of the ASR model or the beam-search decoding algorithm, complicating model reuse and slowing down inference. This work presents a new approach to fast context-biasing with CTC-based Word Spotter (CTC-WS) for CTC and Transducer (RNN-T) ASR models. The proposed method matches CTC log-probabilities against a compact context graph to detect potential context-biasing candidates. The valid candidates then replace their greedy recognition counterparts in corresponding frame intervals. A Hybrid Transducer-CTC model enables the CTC-WS application for the Transducer model. The results demonstrate a significant acceleration of the context-biasing recognition with a simultaneous improvement in F-score and WER compared to baseline methods. The proposed method is publicly available in the NVIDIA NeMo toolkit. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07096 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter Andrusenko, Andrei Laptev, Aleksandr Bataev, Vladimir Lavrukhin, Vitaly Ginsburg, Boris Audio and Speech Processing Artificial Intelligence Computation and Language Machine Learning Sound Accurate recognition of rare and new words remains a pressing problem for contextualized Automatic Speech Recognition (ASR) systems. Most context-biasing methods involve modification of the ASR model or the beam-search decoding algorithm, complicating model reuse and slowing down inference. This work presents a new approach to fast context-biasing with CTC-based Word Spotter (CTC-WS) for CTC and Transducer (RNN-T) ASR models. The proposed method matches CTC log-probabilities against a compact context graph to detect potential context-biasing candidates. The valid candidates then replace their greedy recognition counterparts in corresponding frame intervals. A Hybrid Transducer-CTC model enables the CTC-WS application for the Transducer model. The results demonstrate a significant acceleration of the context-biasing recognition with a simultaneous improvement in F-score and WER compared to baseline methods. The proposed method is publicly available in the NVIDIA NeMo toolkit. |
| title | Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter |
| topic | Audio and Speech Processing Artificial Intelligence Computation and Language Machine Learning Sound |
| url | https://arxiv.org/abs/2406.07096 |