Non-linear filtering in reproducing Kernel Hilbert Spaces for noise-robust speaker verification
| Autor: | Shantanu Chakrabartty, Amin Fazel |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Iterative method
business.industry Speech recognition Feature extraction Hilbert space Pattern recognition Speech processing Speaker recognition symbols.namesake Computer Science::Sound Robustness (computer science) symbols Mel-frequency cepstrum Artificial intelligence business Reproducing kernel Hilbert space Mathematics |
| Zdroj: | ISCAS |
| DOI: | 10.1109/iscas.2009.5117698 |
| Popis: | In this paper, we present a non-linear filtering approach for extracting noise-robust speech features that can be used in a speaker verification task. At the core of the proposed approach is a time-series regression using Reproducing Kernel Hilbert Space (RKHS) based methods that extracts discriminatory non-linear signatures while filtering out the non-informative noise components. A linear projection is then used to map the characteristics of the RKHS regression function into a linear-predictive vector which is then presented as an input to a back-end speaker verification engine. Experiments using the YOHO speaker verification corpus demonstrate that a recognition system trained using the proposed features demonstrate consistent improvements over an equivalent Mel-frequency cepstral coefficients (MFCCs) based verification system for signal-to-noise levels ranging from 0 – 30dB. |
| Databáze: | OpenAIRE |
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