Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

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.


Published in:
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Presented at:
The 36th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Prague, Czech Republic, May 22-27, 2011
Year:
2011
Publisher:
IEEE Service Center, 445 Hoes Lane, PO Box 1331, Piscataway, NJ 08855-1331 USA
Keywords:
Note:
awarded by IEEE Spoken Language Processing
Laboratories:




 Record created 2012-05-01, last modified 2018-03-18

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