Analysis and design of Wavelet-Packet Cepstral coefficients for automatic speech recognition

作者：Pavez Eduardo; Silva Jorge F^*

来源：Speech Communication, 2012, 54(6): 814-835.

DOI：10.1016/j.specom.2012.02.002

• Wavelet Packets
• Filter-bank analysis
• Automatic speech recognition
• Filter-bank selection
• Cepstral coefficients
• The Gray code

This work proposes using Wavelet-Packet Cepstral coefficients (WPPCs) as an alternative way to do filter-bank energy-based feature extraction (FE) for automatic speech recognition (ASR). The rich coverage of time-frequency properties of Wavelet Packets (WPs) is used to obtain new sets of acoustic features, in which competitive and better performances are obtained with respect to the widely adopted Mel-Frequency Cepstral coefficients (MFCCs) in the TIMIT corpus. In the analysis, concrete filter-bank design considerations are stipulated to obtain most of the phone-discriminating information embedded in the speech signal, where the filter-bank frequency selectivity, and better discrimination in the lower frequency range [200 Hz-1 kHz] of the acoustic spectrum are important aspects to consider.

• 出版日期2012-7