Asaei, AfsanehBourlard, HervéCevher, Volkan2011-05-192011-05-192011-05-192011https://infoscience.epfl.ch/handle/20.500.14299/67521We 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.Model-Based Compressive SensingMulti-party Speech RecognitionOverlapping SpeechSparse Component AnalysisSparse Signal Recoverytext::report