SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866908403879641088 |
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| author | Attia, Ahmed Adel Liu, Jing Espy-Wilson, Carl |
| author_facet | Attia, Ahmed Adel Liu, Jing Espy-Wilson, Carl |
| contents | The scarcity of large-scale classroom speech data has hindered the development of AI-driven speech models for education. Public classroom datasets remain limited, and the lack of a dedicated classroom noise corpus prevents the use of standard data augmentation techniques.
In this paper, we introduce a scalable methodology for synthesizing classroom noise using game engines, a framework that extends to other domains. Using this methodology, we present SimClass, a dataset that includes both a synthesized classroom noise corpus and a simulated classroom speech dataset. The speech data is generated by pairing a public children's speech corpus with YouTube lecture videos to approximate real classroom interactions in clean conditions. Our experiments on clean and noisy speech demonstrate that SimClass closely approximates real classroom speech, making it a valuable resource for developing robust speech recognition and enhancement models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09206 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research Attia, Ahmed Adel Liu, Jing Espy-Wilson, Carl Sound Artificial Intelligence Computation and Language Audio and Speech Processing The scarcity of large-scale classroom speech data has hindered the development of AI-driven speech models for education. Public classroom datasets remain limited, and the lack of a dedicated classroom noise corpus prevents the use of standard data augmentation techniques. In this paper, we introduce a scalable methodology for synthesizing classroom noise using game engines, a framework that extends to other domains. Using this methodology, we present SimClass, a dataset that includes both a synthesized classroom noise corpus and a simulated classroom speech dataset. The speech data is generated by pairing a public children's speech corpus with YouTube lecture videos to approximate real classroom interactions in clean conditions. Our experiments on clean and noisy speech demonstrate that SimClass closely approximates real classroom speech, making it a valuable resource for developing robust speech recognition and enhancement models. |
| title | SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research |
| topic | Sound Artificial Intelligence Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.09206 |