An audio-quality-based multi-strategy approach for target speaker extraction in the MISP 2023 Challenge
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913256520548352 |
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| author | Han, Runduo Yan, Xiaopeng Xu, Weiming Guo, Pengcheng Sun, Jiayao Wang, He Lu, Quan Jiang, Ning Xie, Lei |
| author_facet | Han, Runduo Yan, Xiaopeng Xu, Weiming Guo, Pengcheng Sun, Jiayao Wang, He Lu, Quan Jiang, Ning Xie, Lei |
| contents | This paper describes our audio-quality-based multi-strategy approach for the audio-visual target speaker extraction (AVTSE) task in the Multi-modal Information based Speech Processing (MISP) 2023 Challenge. Specifically, our approach adopts different extraction strategies based on the audio quality, striking a balance between interference removal and speech preservation, which benifits the back-end automatic speech recognition (ASR) systems. Experiments show that our approach achieves a character error rate (CER) of 24.2% and 33.2% on the Dev and Eval set, respectively, obtaining the second place in the challenge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03697 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An audio-quality-based multi-strategy approach for target speaker extraction in the MISP 2023 Challenge Han, Runduo Yan, Xiaopeng Xu, Weiming Guo, Pengcheng Sun, Jiayao Wang, He Lu, Quan Jiang, Ning Xie, Lei Sound Audio and Speech Processing This paper describes our audio-quality-based multi-strategy approach for the audio-visual target speaker extraction (AVTSE) task in the Multi-modal Information based Speech Processing (MISP) 2023 Challenge. Specifically, our approach adopts different extraction strategies based on the audio quality, striking a balance between interference removal and speech preservation, which benifits the back-end automatic speech recognition (ASR) systems. Experiments show that our approach achieves a character error rate (CER) of 24.2% and 33.2% on the Dev and Eval set, respectively, obtaining the second place in the challenge. |
| title | An audio-quality-based multi-strategy approach for target speaker extraction in the MISP 2023 Challenge |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2401.03697 |