SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research

TL;DR

This paper introduces SimClass, a novel synthetic classroom speech dataset created using game engine simulations, addressing the lack of large-scale classroom speech data for improving speech recognition in educational settings.

Contribution

The paper presents a scalable methodology for generating classroom noise and speech data via game engine simulation, providing a new resource for ASR research in education.

Findings

SimClass closely approximates real classroom speech.

Synthetic data improves robustness of speech recognition models.

The methodology extends to other domains for data synthesis.

Abstract

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.