A Study on Speech Endpoint Detection and Applications

碩士 === 大葉大學 === 電機工程學系 === 103 === In this research we discuss the effect between endpoint detection and speech recognition. First we refer to HTK’s endpoint detection. We use MATLAB to design a program to change the threshold constants for five kinds of SNR wave files. Each SNR wave files can obt...

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Main Authors: Li Wen-Zuo, 李文祚
Other Authors: Lee Lee-Min
Format: Others
Language:zh-TW
Published: 2015
Online Access:http://ndltd.ncl.edu.tw/handle/38763613104682958616
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spelling ndltd-TW-103DYU004420312016-07-16T04:11:57Z http://ndltd.ncl.edu.tw/handle/38763613104682958616 A Study on Speech Endpoint Detection and Applications 語音端點偵測與應用 Li Wen-Zuo 李文祚 碩士 大葉大學 電機工程學系 103 In this research we discuss the effect between endpoint detection and speech recognition. First we refer to HTK’s endpoint detection. We use MATLAB to design a program to change the threshold constants for five kinds of SNR wave files. Each SNR wave files can obtain a best threshold constant. This optimization of threshold constant have 1.2%~3.9% endpoint detection error improvement than HTK’s endpoint detection. But SNR5 and SNR0 wave files have 39.5% and 37.0% endpoint detection error improvement. Then we improve the endpoint detection by adding zero crossing rate endpoint detection. We get 20.8%~46.29% endpoint detection error improvement for all kinds of SNR wave files. Especially on SNR20 wave files have 50.86% endpoint detection error improvement. Finally, we put both HTK’s endpoint detection and our endpoint detection results into a speech recognition system. On the least noise wave files. The recognition rate of HTK’s endpoint detection and our endpoint detection are 98.64% and 99.01%. Each SNR wave files have 0.37%~6.57% recognition rate improvement. Even on SNR10 and SNR5 wave files have 31.94% and 21.49% improvement of recognition rate. Lee Lee-Min 李立民 2015 學位論文 ; thesis 61 zh-TW
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language zh-TW
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description 碩士 === 大葉大學 === 電機工程學系 === 103 === In this research we discuss the effect between endpoint detection and speech recognition. First we refer to HTK’s endpoint detection. We use MATLAB to design a program to change the threshold constants for five kinds of SNR wave files. Each SNR wave files can obtain a best threshold constant. This optimization of threshold constant have 1.2%~3.9% endpoint detection error improvement than HTK’s endpoint detection. But SNR5 and SNR0 wave files have 39.5% and 37.0% endpoint detection error improvement. Then we improve the endpoint detection by adding zero crossing rate endpoint detection. We get 20.8%~46.29% endpoint detection error improvement for all kinds of SNR wave files. Especially on SNR20 wave files have 50.86% endpoint detection error improvement. Finally, we put both HTK’s endpoint detection and our endpoint detection results into a speech recognition system. On the least noise wave files. The recognition rate of HTK’s endpoint detection and our endpoint detection are 98.64% and 99.01%. Each SNR wave files have 0.37%~6.57% recognition rate improvement. Even on SNR10 and SNR5 wave files have 31.94% and 21.49% improvement of recognition rate.
author2 Lee Lee-Min
author_facet Lee Lee-Min
Li Wen-Zuo
李文祚
author Li Wen-Zuo
李文祚
spellingShingle Li Wen-Zuo
李文祚
A Study on Speech Endpoint Detection and Applications
author_sort Li Wen-Zuo
title A Study on Speech Endpoint Detection and Applications
title_short A Study on Speech Endpoint Detection and Applications
title_full A Study on Speech Endpoint Detection and Applications
title_fullStr A Study on Speech Endpoint Detection and Applications
title_full_unstemmed A Study on Speech Endpoint Detection and Applications
title_sort study on speech endpoint detection and applications
publishDate 2015
url http://ndltd.ncl.edu.tw/handle/38763613104682958616
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