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

被引：32

作者：

Pavez, Eduardo ^{[1
]}

Silva, Jorge F. ^{[1
]}

机构：

[1] Univ Chile, Dept Elect Engn, Santiago 4123, Chile

来源：

SPEECH COMMUNICATION | 2012年 / 54卷 / 06期

关键词：

Wavelet Packets; Filter-bank analysis; Automatic speech recognition; Filter-bank selection; Cepstral coefficients; The Gray code; SAMPLING THEOREM; MARKOV-MODELS; SIGNAL; REPRESENTATIONS; FILTERS;

D O I：

10.1016/j.specom.2012.02.002

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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. (C) 2012 Elsevier B.V. All rights reserved.

引用

页码：814 / 835

页数：22

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