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A sticky HDP-HMM with application to speaker diarization
2011
Annals of Applied Statistics
We consider the problem of speaker diarization, the problem of segmenting an audio recording of a meeting into temporal segments corresponding to individual speakers. The problem is rendered particularly difficult by the fact that we are not allowed to assume knowledge of the number of people participating in the meeting. To address this problem, we take a Bayesian nonparametric approach to speaker diarization that builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of
doi:10.1214/10-aoas395
fatcat:2ffqxbaocjf5hapn6zjkgexf3e