Cross-utterance ASR Rescoring with Graph-based Label Propagation
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909098147053568 |
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| author | Tankasala, Srinath Chen, Long Stolcke, Andreas Raju, Anirudh Deng, Qianli Chandak, Chander Khare, Aparna Maas, Roland Ravichandran, Venkatesh |
| author_facet | Tankasala, Srinath Chen, Long Stolcke, Andreas Raju, Anirudh Deng, Qianli Chandak, Chander Khare, Aparna Maas, Roland Ravichandran, Venkatesh |
| contents | We propose a novel approach for ASR N-best hypothesis rescoring with graph-based label propagation by leveraging cross-utterance acoustic similarity. In contrast to conventional neural language model (LM) based ASR rescoring/reranking models, our approach focuses on acoustic information and conducts the rescoring collaboratively among utterances, instead of individually. Experiments on the VCTK dataset demonstrate that our approach consistently improves ASR performance, as well as fairness across speaker groups with different accents. Our approach provides a low-cost solution for mitigating the majoritarian bias of ASR systems, without the need to train new domain- or accent-specific models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_15132 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Cross-utterance ASR Rescoring with Graph-based Label Propagation Tankasala, Srinath Chen, Long Stolcke, Andreas Raju, Anirudh Deng, Qianli Chandak, Chander Khare, Aparna Maas, Roland Ravichandran, Venkatesh Audio and Speech Processing Computation and Language Machine Learning Sound We propose a novel approach for ASR N-best hypothesis rescoring with graph-based label propagation by leveraging cross-utterance acoustic similarity. In contrast to conventional neural language model (LM) based ASR rescoring/reranking models, our approach focuses on acoustic information and conducts the rescoring collaboratively among utterances, instead of individually. Experiments on the VCTK dataset demonstrate that our approach consistently improves ASR performance, as well as fairness across speaker groups with different accents. Our approach provides a low-cost solution for mitigating the majoritarian bias of ASR systems, without the need to train new domain- or accent-specific models. |
| title | Cross-utterance ASR Rescoring with Graph-based Label Propagation |
| topic | Audio and Speech Processing Computation and Language Machine Learning Sound |
| url | https://arxiv.org/abs/2303.15132 |