Similarity Measurement of Segment-Level Speaker Embeddings in Speaker Diarization

Abstract

In this paper, we propose a neural-network-based similarity measurement method to learn the similarity between any two speaker embeddings, where both previous and future contexts are considered. Moreover, we propose the segmental pooling strategy and jointly train the speaker embedding network along with the similarity measurement model. Later, this joint training framework is further extended to the target-speaker voice activity detection (TS-VAD), with only slight modification in the network architecture. Experimental results of the DIHARD II, DIHARD III and VoxConverse datasets show that our clustering-based system with the neural similarity measurement achieves superior performance to recent approaches on all three datasets. In addition, the segment-level TS-VAD method further improves the clustering-based results and achieves DER of 16.48%, 11.62% and 4.39% on the DIHARD II, DIHARD III and VoxConverse datasets, respectively.