CONF
Asaei_ICASSP_2011/IDIAP
Model-based Compressive Sensing for Multi-party Distant Speech Recognition
Asaei, Afsaneh
Bourlard, Hervé
Cevher, Volkan
Model-Based Compressive Sensing
Overlapping Speech
Sparse Component Analysis
Sparse Recovery
speech recognition
EXTERNAL
https://publications.idiap.ch/attachments/papers/2011/Asaei_ICASSP_2011.pdf
PUBLIC
https://publications.idiap.ch/index.php/publications/showcite/Asaei_Idiap-RR-04-2011
Related documents
2011 IEEE International Conference on Acoustics, Speech and Signal Processing
2011
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.
REPORT
Asaei_Idiap-RR-04-2011/IDIAP
Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition
Asaei, Afsaneh
Bourlard, Hervé
Cevher, Volkan
Model-Based Compressive Sensing
Multi-party Speech Recognition
Overlapping Speech
Sparse Component Analysis
Sparse Signal Recovery
EXTERNAL
https://publications.idiap.ch/attachments/reports/2011/Asaei_Idiap-RR-04-2011.pdf
PUBLIC
Idiap-RR-04-2011
2011
Idiap
March 2011
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.