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Model-based compressive sensing for multi-party distant speech recognition (2011)
| Content Provider | CiteSeerX |
|---|---|
| Author | Asaei, Afsaneh Bourlard, Herve ́ Cevher, Volkan |
| Description | In Proceedings of ICASSP We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for effi-cient recovery of convolutive speech mixtures in spectro-temporal domain. Compared to the common sparse component analysis tech-niques, 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. |
| File Format | |
| Language | English |
| Publisher Date | 2011-01-01 |
| Access Restriction | Open |
| Subject Keyword | Speech Recognition Convolutive Speech Mixture Novel Source Separation Algorithm Multi-party Distant Speech Recognition Substantial Improvement Compressive Sensing Sparsity Model Sparse Recovery Formulation Model-based Compressive Sensing Recent Algorithmic Advance Effi-cient Recovery Common Sparse Component Analysis Tech-niques Multi-party Scenario Target Speaker Spectro-temporal Domain |
| Content Type | Text |
| Resource Type | Article |
