A speech endpoint detection algorithm based on wavelet transforms

Author

Cao Yali ; La Dongsheng ; Jia Shuo ; Niu Xuefen

Author_Institution

Northeastern Univ. at Qinhuangdao, Qinhuangdao, China

fYear

2014

fDate

May 31 2014-June 2 2014

Firstpage

3010

Lastpage

3012

Abstract

This paper highlights the sub-band average energy variance (SBAEV) approach to perform the endpoint detection process, which involves the segmentation of speech signals from non-speech signals. The SBAEV models have been proposed to perform endpoint detections of isolated digit utterances spoken in the Language. Experiment results obtained from this method are acoustically verified, visually checked and compared to the conventional method of endpoint detection. It was found that the endpoint detection accuracy using the SBAEV approach is very high and encouraging..

Keywords

feature extraction; speech processing; speech recognition; wavelet transforms; SBAEV; endpoint detection accuracy; endpoint detection process; speech endpoint detection algorithm; speech recognition; speech signal segmentation; subband average energy variance; wavelet transforms; Noise; Speech; Speech processing; Wavelet analysis; Wavelet domain; Wavelet transforms; endpoint detection; sub-band average energy variance; wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (2014 CCDC), The 26th Chinese

Conference_Location

Changsha

Print_ISBN

978-1-4799-3707-3

Type

conf

DOI

10.1109/CCDC.2014.6852690

Filename

6852690