Dual-Stage Training Frame-level Overlapping Speech Detection Model Based on Convolutional Neural Network Architecture

A dual-stage training frame-level overlapping speech detection model is proposed in this paper, with its core being based on RepVGG blocks structure, and RepVGG is built on simple one-dimensional Convolutional Neural Networks (CNNs). Both the learning of local features and temporal features of acoustic signals are accomplished by the RepVGG structure. The model categorizes speech into triple classes: non-speech frames, non-overlapping speech frames, and overlapping speech frames. Firstly, RepVGG blocks are employed in the construction of a Speech Activity Detection (SAD) model to detect non-speech and speech frames. Subsequently, a Overlapping Speech Detection (OSD) model is constructed using RepVGG blocks and is trained with real labels to differentiate between non-overlapping and overlapping speech frames. Finally, these two models are combined and fine-tuned to directly output classification results. The proposed overlapping speech detection model achieves advanced performance on the AliMeeting dataset with fewer parameters.