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