Date
Publisher
arXiv
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.
Study at a glance
- Question: Can game engines simulate realistic classroom speech and noise to help develop speech recognition tools for education?
- Setting and data: A public children's speech corpus, YouTube lecture videos, and simulated classroom noise generated using game engines.
- Method: Simulation of classroom audio using game engines combined with existing speech and video recordings, tested on speech models.
Why it matters
A 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 standard data augmentation techniques.
Key findings
- Simulated speech combining children's voices and online lectures closely approximates real classroom speech in both quiet and noisy conditions.
- Game engine simulation produces realistic, scalable classroom background noise for developing educational speech tools.
- Synthesized classroom audio provides an effective resource for training speech-recognition and sound-enhancement models.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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