Abstract / truncated to 115 words (read the full abstract)

In this work, we study the application of wavelet analysis for robust speech processing. Reliable time-scale features (TS) which characterize the relevant phonetic classes such as voiced (V), unvoiced (UV), silence (S), mixed-excitation, and stop sounds are extracted. By training neural and Bayesian networks, the classification rates provided by only 7 TS features are mostly similar to the ones obtained by 13 MFCC features. The TS features are further enhanced to design a reliable and low-complexity V/UV/S classifier. Quantile filtering and slope tracking are used for deriving adaptive thresholds. A robust voice activity detector is then built and used as a pre-processing stage to improve the performance of a speaker verification system. Based on wavelet ... toggle 7 keywords

wavelet analysis wavelet denoising robust speech processing speech enhancement speech classification noise reduction ASR

Information

Author
Pham, Van Tuan
Institution
Graz University of Technology
Supervisor
Publication Year
2007
Upload Date
Sept. 8, 2010

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