SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research
This addresses the problem of limited data for developing robust speech recognition models in educational settings, though it is incremental as it builds on existing methods for data synthesis.
The paper tackles the scarcity of classroom speech data for AI-driven education models by introducing SimClass, a dataset synthesized using game engines and public resources, which experiments show closely approximates real classroom speech.
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.