Enhancing Low-Latency Speaker Diarization with Spatial Dictionary Learning

Abstract

This study proposes a low-latency online speaker diarization framework. Specifically, we design a spatial dictionary learning module shared across different frequency bands, enabling spatial feature learning at each frequency bin. This contributes to reducing the latency constraints of the online diarization system. Additionally, a magnitude-weighted fusion is devised to integrate spectral features. Consequently, the system can extract discriminative speaker embeddings by simultaneously considering spectral and spatial features. Experimental results on the Alimeeting dataset demonstrate a significant improvement in diarization error rates across various latencies, with a relative improvement of 45.80% compared to single-channel online diarization. Moreover, our method surpasses offline direction-of-arrival-based diarization and achieves comparable performance to the second-ranked offline system of the Alimeeting challenge.