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  4. Model-based Compressive Sensing for Multi-party Distant Speech Recognition
 
conference paper not in proceedings

Model-based Compressive Sensing for Multi-party Distant Speech Recognition

Asaei, Afsaneh  
•
Bourlard, Hervé  
•
Cevher, Volkan
2011
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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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