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An EM Algorithm for Joint Source Separation and Diarisation of Multichannel Convolutive Speech Mixtures
Abstract : We present a probabilistic model for joint source separation and diarisation of multichannel convolutive speech mixtures. We build upon the framework of local Gaussian model (LGM) with non-negative matrix factorization (NMF). The diarisa-tion is introduced as a temporal labeling of each source in the mix as active or inactive at the short-term frame level. We devise an EM algorithm in which the source separation process is aided by the diarisation state, since the latter indicates the sources actually present in the mixture. The diarisation state is tracked with a Hidden Markov Model (HMM) with emission probabilities calculated from the estimated source signals. The proposed EM has separation performance comparable with a state-of-the-art LGM NMF method, while out-performing a state-of-the-art speaker diarisation pipeline.
Cited literature [14 references]
https://hal.inria.fr/hal-01430761
Contributor : Team Perception Connect in order to contact the contributor
Submitted on : Tuesday, January 10, 2017 - 11:24:31 AM
Last modification on : Tuesday, October 19, 2021 - 11:27:13 AM
Long-term archiving on: : Tuesday, April 11, 2017 - 2:14:03 PM
Contributor : Team Perception Connect in order to contact the contributor
Submitted on : Tuesday, January 10, 2017 - 11:24:31 AM
Last modification on : Tuesday, October 19, 2021 - 11:27:13 AM
Long-term archiving on: : Tuesday, April 11, 2017 - 2:14:03 PM
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diarisation_camready.pdf
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