| Autor: |
Gajić, Bojana, Paliwal, Kuldip K. |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Audio, Speech & Language Processing; Mar2006, Vol. 14 Issue 2, p600-608, 9p, 3 Diagrams, 4 Charts, 4 Graphs |
| Abstrakt: |
We investigate how dominant-frequency information can be used in speech feature extraction to increase the robustness of automatic speech recognition against additive background noise. First, we review several earlier proposed auditory-based feature extraction methods and argue that the use of dominant. frequency information might be one of the major reasons for their improved noise robustness. Furthermore, we propose a new feature extraction method, which combines subband power information with dominant subband frequency information in a simple and computationally efficient way. The proposed features are shown to be considerably more robust against additive background noise than standard reel-frequency cepstrum coefficients on two different recognition tasks. The performance improvement increased as we moved from a small-vocabulary isolated-word task to a medium-vocabulary continuous-speech task, where the proposed features also outperformed a computationally expensive auditory-based method. The greatest improvement was obtained for noise types characterized by a relatively flat spectral density. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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