Exploiting the Intermittency of Speech for Joint Separation and Diarization

Dionyssos Kounades-Bastian 1 Laurent Girin 2, 1 Xavier Alameda-Pineda 1 Radu Horaud 1 Sharon Gannot 3
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 GIPSA-CRISSP - CRISSP
GIPSA-DPC - Département Parole et Cognition
Abstract : Natural conversations are spontaneous exchanges involving two or more people speaking in an intermittent manner. Therefore one expects such conversation to have intervals where some of the speakers are silent. Yet, most (multichannel) audio source separation (MASS) methods consider the sound sources to be continuously emitting on the total duration of the processed mixture. In this paper we propose a probabilistic model for MASS where the sources may have pauses. The activity of the sources is modeled as a hidden state, the diarization state, enabling us to activate/de-activate the sound sources at time frame resolution. We plug the diarization model within the spatial covariance matrix model proposed for MASS, and obtain an improvement in performance over the state of the art when separating mixtures with intermittent speakers.
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Dionyssos Kounades-Bastian, Laurent Girin, Xavier Alameda-Pineda, Radu Horaud, Sharon Gannot. Exploiting the Intermittency of Speech for Joint Separation and Diarization. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, Oct 2017, New Paltz, NY, United States. pp.41-45, ⟨10.1109/WASPAA.2017.8169991⟩. ⟨hal-01568813⟩

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