Automatic English Stop Consonants Classification using Wavelet Analysis and Hidden Markov Models

被引：0
|
作者：
Kuehne, Marco [1 ]
Togneri, Roberto [1 ]
机构：
[1] Univ Western Australia, Sch Elect Elect & Comp Engn, CIIPS, Nedlands, WA 6009, Australia
来源：
INTERSPEECH 2006 AND 9TH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING, VOLS 1-5 | 2006年
关键词：
speech recognition; wavelet analysis; Hidden Markov Models;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper compares wavelet and STFT analysis for a speaker-independent stop classification task using the TIMIT database. In the designed experiment the HMM classifier had to assign each test token to one of the following stop classes [d,g,b,t,k,p,dx]. On 6332 stops the wavelet features obtained an overall accuracy of 86 % which corresponds to a 14 % relative error reduction compared to the STFT baseline system. Furthermore an analysis of the HMM misclassifications revealed that voiced stops were highly confused with their voiceless unaspirated counterparts.
引用
收藏
页码：637 / 640
页数：4
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