用游戏引擎合成教室噪声与混响，构建可扩展的课堂语音数据集
RealClass: A Framework for Classroom Speech Simulation with Public Datasets and Game Engines
- 通过游戏引擎模拟教室环境噪声和混响响应，实现数据生成
- 合成数据在清音与噪杂场景下均逼近真实课堂语音表现
- 适合教育AI语音模型训练，尤其缺真实数据时使用
课堂语音数据稀缺制约了教育类语音AI模型的发展。现有课堂数据集规模有限且未公开，缺乏专用的教室噪声或房间冲击响应（RIR）数据集，导致无法使用标准数据增强技术。本文提出一种可扩展的方法，利用游戏引擎合成教室噪声与RIR，构建了一个通用框架，可拓展至其他场景。基于该方法，我们发布RealClass数据集，包含合成的教室噪声语料库及从公开数据集中整理的课堂语音数据。语音数据将儿童语音语料与来自YouTube视频的授课语音配对，以模拟真实教室中的清晰条件互动。在清音与噪声环境下实验表明，RealClass能有效逼近真实课堂语音特性，是缺乏大量真实课堂语音数据时的重要补充。
原文摘要 · Abstract (English)
The scarcity of large-scale classroom speech data has hindered the development of AI-driven speech models for education. Classroom datasets remain limited and not publicly available, and the absence of dedicated classroom noise or Room Impulse Response (RIR) corpora prevents the use of standard data augmentation techniques. In this paper, we introduce a scalable methodology for synthesizing classroom noise and RIRs using game engines, a versatile framework that can extend to other domains beyond the classroom. Building on this methodology, we present RealClass, a dataset that combines a synthesized classroom noise corpus with a classroom speech dataset compiled from publicly available corpora. The speech data pairs a children's speech corpus with instructional speech extracted from YouTube videos to approximate real classroom interactions in clean conditions. Experiments on clean and noisy speech show that RealClass closely approximates real classroom speech, making it a valuable asset in the absence of abundant real classroom speech.
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