LV-CTC: Non-autoregressive ASR with CTC and latent variable models
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910388706082816 |
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| author | Fujita, Yuya Watanabe, Shinji Chang, Xuankai Maekaku, Takashi |
| author_facet | Fujita, Yuya Watanabe, Shinji Chang, Xuankai Maekaku, Takashi |
| contents | Non-autoregressive (NAR) models for automatic speech recognition (ASR) aim to achieve high accuracy and fast inference by simplifying the autoregressive (AR) generation process of conventional models. Connectionist temporal classification (CTC) is one of the key techniques used in NAR ASR models. In this paper, we propose a new model combining CTC and a latent variable model, which is one of the state-of-the-art models in the neural machine translation research field. A new neural network architecture and formulation specialized for ASR application are introduced. In the proposed model, CTC alignment is assumed to be dependent on the latent variables that are expected to capture dependencies between tokens. Experimental results on a 100 hours subset of Librispeech corpus showed the best recognition accuracy among CTC-based NAR models. On the TED-LIUM2 corpus, the best recognition accuracy is achieved including AR E2E models with faster inference speed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19207 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LV-CTC: Non-autoregressive ASR with CTC and latent variable models Fujita, Yuya Watanabe, Shinji Chang, Xuankai Maekaku, Takashi Audio and Speech Processing Non-autoregressive (NAR) models for automatic speech recognition (ASR) aim to achieve high accuracy and fast inference by simplifying the autoregressive (AR) generation process of conventional models. Connectionist temporal classification (CTC) is one of the key techniques used in NAR ASR models. In this paper, we propose a new model combining CTC and a latent variable model, which is one of the state-of-the-art models in the neural machine translation research field. A new neural network architecture and formulation specialized for ASR application are introduced. In the proposed model, CTC alignment is assumed to be dependent on the latent variables that are expected to capture dependencies between tokens. Experimental results on a 100 hours subset of Librispeech corpus showed the best recognition accuracy among CTC-based NAR models. On the TED-LIUM2 corpus, the best recognition accuracy is achieved including AR E2E models with faster inference speed. |
| title | LV-CTC: Non-autoregressive ASR with CTC and latent variable models |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2403.19207 |