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MODEL-BASED COMPRESSIVE SENSING FOR MULTI-PARTY DISTANT SPEECH RECOGNITION

Full Paper at IEEE Xplore

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Presented by: Afsaneh Asaei, Author(s): Afsaneh Asaei, Hervé Bourlard, Volkan Cevher, Idiap Research Institute / Ecole Polytechnique Federale de Lausanne, Switzerland

We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for efficient recovery of convolutive speech mixtures in spectro-temporal domain. Compared to the common sparse component analysis techniques, our approach fully exploits structured sparsity models to obtain substantial improvement over the existing state-of-the-art. We evaluate our method for separation and recognition of a target speaker in a multi-party scenario. Our results provide compelling evidence of the effectiveness of sparse recovery formulations in speech recognition.

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  Lecture Information

Recorded: 2011-05-26 17:35 - 17:55, Panorama
Added: 15. 6. 2011 19:18
Number of views: 43
Video resolution: 1024x576 px, 512x288 px
Video length: 0:18:54
Audio track: MP3 [6.00 MB], 0:18:54