Speech Activity Detection for Multi-Party Conversation Analyses Based on Likelihood Ratio Test on Spatial Magnitude

This paper proposes a microphone array-based speech activity detection (SAD) method for analyzing multi-party conversations recorded in the presence of noise. In particular, the proposed method considers conversations where the number of speakers and speaker locations cannot be restricted, such as when standing and talking, and at poster sessions. When we observe such conversations, there are directional noise sources and diffuse noise that affect the direction of arrival estimations of the target speech signals. To detect speech activity without a priori knowledge about the speakers and noise environments, a likelihood ratio test (LRT)-based SAD method is applied to spatial magnitude, which are estimated by using the time-frequency masking of the observed spectra. The proposed method can exploit the enhanced signals obtained from time-frequency masking, and works even in the presence of environmental noise. Experiments with recorded simulated poster sessions confirmed that the proposed method could outperform conventional methods based on the LRT for a single channel, magnitude coherence, or crosspower spectrum phase.