Target word activity detector: An approach to obtain ASR word boundaries without lexicon
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916405548417024 |
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| author | Sivasankaran, Sunit Sun, Eric Li, Jinyu Huang, Yan Pan, Jing |
| author_facet | Sivasankaran, Sunit Sun, Eric Li, Jinyu Huang, Yan Pan, Jing |
| contents | Obtaining word timestamp information from end-to-end (E2E) ASR models remains challenging due to the lack of explicit time alignment during training. This issue is further complicated in multilingual models. Existing methods, either rely on lexicons or introduce additional tokens, leading to scalability issues and increased computational costs. In this work, we propose a new approach to estimate word boundaries without relying on lexicons. Our method leverages word embeddings from sub-word token units and a pretrained ASR model, requiring only word alignment information during training. Our proposed method can scale-up to any number of languages without incurring any additional cost. We validate our approach using a multilingual ASR model trained on five languages and demonstrate its effectiveness against a strong baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13913 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Target word activity detector: An approach to obtain ASR word boundaries without lexicon Sivasankaran, Sunit Sun, Eric Li, Jinyu Huang, Yan Pan, Jing Computation and Language Sound Audio and Speech Processing Obtaining word timestamp information from end-to-end (E2E) ASR models remains challenging due to the lack of explicit time alignment during training. This issue is further complicated in multilingual models. Existing methods, either rely on lexicons or introduce additional tokens, leading to scalability issues and increased computational costs. In this work, we propose a new approach to estimate word boundaries without relying on lexicons. Our method leverages word embeddings from sub-word token units and a pretrained ASR model, requiring only word alignment information during training. Our proposed method can scale-up to any number of languages without incurring any additional cost. We validate our approach using a multilingual ASR model trained on five languages and demonstrate its effectiveness against a strong baseline. |
| title | Target word activity detector: An approach to obtain ASR word boundaries without lexicon |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2409.13913 |