%Aigaion2 BibTeX export from Idiap Publications
%Tuesday 21 January 2025 05:23:17 AM

@INPROCEEDINGS{Asaei_ICASSP_2011,
         author = {Asaei, Afsaneh and Bourlard, Herv{\'{e}} and Cevher, Volkan},
       keywords = {Model-Based Compressive Sensing, Overlapping Speech, Sparse Component Analysis, Sparse Recovery, speech recognition},
       projects = {Idiap, FP 7},
          title = {Model-based Compressive Sensing for Multi-party Distant Speech Recognition},
      booktitle = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing},
           year = {2011},
       crossref = {Asaei_Idiap-RR-04-2011},
       abstract = {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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2011/Asaei_ICASSP_2011.pdf}
}



crossreferenced publications: 
@TECHREPORT{Asaei_Idiap-RR-04-2011,
         author = {Asaei, Afsaneh and Bourlard, Herv{\'{e}} and Cevher, Volkan},
       keywords = {Model-Based Compressive Sensing, Multi-party Speech Recognition, Overlapping Speech, Sparse Component Analysis, Sparse Signal Recovery},
       projects = {Idiap},
          month = {3},
          title = {Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition},
           type = {Idiap-RR},
         number = {Idiap-RR-04-2011},
           year = {2011},
    institution = {Idiap},
       abstract = {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.},
            pdf = {https://publications.idiap.ch/attachments/reports/2011/Asaei_Idiap-RR-04-2011.pdf}
}