Optimizing Bayesian Hmm Based X-Vector Clustering For The Second Dihard Speech Diarization Challenge

This paper presents an analysis of our diarization system winning the second DIHARD speech diarization challenge, track 1. This system is based on clustering x-vector speaker embeddings extracted every 0.25s from short segments of the input recording. In
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