Bayesian HMM Based x-Vector Clustering for Speaker Diarization

Published: 01 Jan 2019, Last Modified: 22 May 2024INTERSPEECH 2019EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: This paper presents a simplified version of the previously proposed diarization algorithm based on Bayesian Hidden Markov Models, which uses Variational Bayesian inference for very fast and robust clustering of x-vector (neural network based speaker embeddings). The presented results show that this clustering algorithm provides significant improvements in diarization performance as compared to the previously used Agglomerative Hierarchical Clustering. The output of this system can be further employed as an initialization for a second stage VB diarization system, using frame-wise MFCC features as input, to obtain optimal results.
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