Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914943988662272 |
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| author | Xue, Hongfei Gong, Rong Shao, Mingchen Xu, Xin Wang, Lezhi Xie, Lei Bu, Hui Zhou, Jiaming Qin, Yong Du, Jun Li, Ming Zhang, Binbin Jia, Bin |
| author_facet | Xue, Hongfei Gong, Rong Shao, Mingchen Xu, Xin Wang, Lezhi Xie, Lei Bu, Hui Zhou, Jiaming Qin, Yong Du, Jun Li, Ming Zhang, Binbin Jia, Bin |
| contents | The StutteringSpeech Challenge focuses on advancing speech technologies for people who stutter, specifically targeting Stuttering Event Detection (SED) and Automatic Speech Recognition (ASR) in Mandarin. The challenge comprises three tracks: (1) SED, which aims to develop systems for detection of stuttering events; (2) ASR, which focuses on creating robust systems for recognizing stuttered speech; and (3) Research track for innovative approaches utilizing the provided dataset. We utilizes an open-source Mandarin stuttering dataset AS-70, which has been split into new training and test sets for the challenge. This paper presents the dataset, details the challenge tracks, and analyzes the performance of the top systems, highlighting improvements in detection accuracy and reductions in recognition error rates. Our findings underscore the potential of specialized models and augmentation strategies in developing stuttered speech technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05430 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge Xue, Hongfei Gong, Rong Shao, Mingchen Xu, Xin Wang, Lezhi Xie, Lei Bu, Hui Zhou, Jiaming Qin, Yong Du, Jun Li, Ming Zhang, Binbin Jia, Bin Audio and Speech Processing Sound The StutteringSpeech Challenge focuses on advancing speech technologies for people who stutter, specifically targeting Stuttering Event Detection (SED) and Automatic Speech Recognition (ASR) in Mandarin. The challenge comprises three tracks: (1) SED, which aims to develop systems for detection of stuttering events; (2) ASR, which focuses on creating robust systems for recognizing stuttered speech; and (3) Research track for innovative approaches utilizing the provided dataset. We utilizes an open-source Mandarin stuttering dataset AS-70, which has been split into new training and test sets for the challenge. This paper presents the dataset, details the challenge tracks, and analyzes the performance of the top systems, highlighting improvements in detection accuracy and reductions in recognition error rates. Our findings underscore the potential of specialized models and augmentation strategies in developing stuttered speech technologies. |
| title | Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2409.05430 |