AS-70: A Mandarin stuttered speech dataset for automatic speech recognition and stuttering event detection
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917690482884608 |
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| author | Gong, Rong Xue, Hongfei Wang, Lezhi Xu, Xin Li, Qisheng Xie, Lei Bu, Hui Wu, Shaomei Zhou, Jiaming Qin, Yong Zhang, Binbin Du, Jun Bin, Jia Li, Ming |
| author_facet | Gong, Rong Xue, Hongfei Wang, Lezhi Xu, Xin Li, Qisheng Xie, Lei Bu, Hui Wu, Shaomei Zhou, Jiaming Qin, Yong Zhang, Binbin Du, Jun Bin, Jia Li, Ming |
| contents | The rapid advancements in speech technologies over the past two decades have led to human-level performance in tasks like automatic speech recognition (ASR) for fluent speech. However, the efficacy of these models diminishes when applied to atypical speech, such as stuttering. This paper introduces AS-70, the first publicly available Mandarin stuttered speech dataset, which stands out as the largest dataset in its category. Encompassing conversational and voice command reading speech, AS-70 includes verbatim manual transcription, rendering it suitable for various speech-related tasks. Furthermore, baseline systems are established, and experimental results are presented for ASR and stuttering event detection (SED) tasks. By incorporating this dataset into the model fine-tuning, significant improvements in the state-of-the-art ASR models, e.g., Whisper and Hubert, are observed, enhancing their inclusivity in addressing stuttered speech. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07256 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AS-70: A Mandarin stuttered speech dataset for automatic speech recognition and stuttering event detection Gong, Rong Xue, Hongfei Wang, Lezhi Xu, Xin Li, Qisheng Xie, Lei Bu, Hui Wu, Shaomei Zhou, Jiaming Qin, Yong Zhang, Binbin Du, Jun Bin, Jia Li, Ming Sound Artificial Intelligence Audio and Speech Processing The rapid advancements in speech technologies over the past two decades have led to human-level performance in tasks like automatic speech recognition (ASR) for fluent speech. However, the efficacy of these models diminishes when applied to atypical speech, such as stuttering. This paper introduces AS-70, the first publicly available Mandarin stuttered speech dataset, which stands out as the largest dataset in its category. Encompassing conversational and voice command reading speech, AS-70 includes verbatim manual transcription, rendering it suitable for various speech-related tasks. Furthermore, baseline systems are established, and experimental results are presented for ASR and stuttering event detection (SED) tasks. By incorporating this dataset into the model fine-tuning, significant improvements in the state-of-the-art ASR models, e.g., Whisper and Hubert, are observed, enhancing their inclusivity in addressing stuttered speech. |
| title | AS-70: A Mandarin stuttered speech dataset for automatic speech recognition and stuttering event detection |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2406.07256 |