The Study of Using Speech Recognition Technology to Help Diagnose for Rhinitis Patients
碩士 === 長庚大學 === 資訊工程研究所 === 94 === In this thesis we use speech recognition technology to recognize rhinitis patients and normal patients. First, we have to build voice database which include rhinitis patients and normal patients. The gender of voice database include female and male. The voice of vo...
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ndltd-TW-094CGU003920152016-06-01T04:14:45Z http://ndltd.ncl.edu.tw/handle/92767189670915068472 The Study of Using Speech Recognition Technology to Help Diagnose for Rhinitis Patients 語音辨識應用於鼻炎輔助診斷之研究 Liao wei chih 廖偉智 碩士 長庚大學 資訊工程研究所 94 In this thesis we use speech recognition technology to recognize rhinitis patients and normal patients. First, we have to build voice database which include rhinitis patients and normal patients. The gender of voice database include female and male. The voice of voice database include /a/ and /m/. Second, we use Mel-Frequency Cepstral Coefficients (MFCC), Formant, Energy, to be different voice features. Third, we use two kinds of classfier. Including Gaussian mixture model (GMM) and Linear Discriminant Analysis (LDA). We use GMM to be acoustic model. LDA is exploited to increase the classification accuracy at a lower dimensional feature vector space. In our experiments, we found MFCC is the best voice feature. Because of MFCC outperforms Formant and Energy. The voice classification accuracy of female /a/ and /m/ are 84.5%. The voice classification accuracy of male /a/ is 69.73% and /m/ is 76.31%. If Lda is applied, MFCC can achieve classification accuracy as follows, The voice classification accuracy of female /a/ is 87.16% and /m/ is 86.8%. The voice classification accuracy of male /a/ is 73.11% and /m/ is 76.83%. Ren-Yuan Lyu 呂仁園 2006 學位論文 ; thesis 59 zh-TW |
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碩士 === 長庚大學 === 資訊工程研究所 === 94 === In this thesis we use speech recognition technology to recognize rhinitis patients and normal patients. First, we have to build voice database which include rhinitis patients and normal patients. The gender of voice database include female and male. The voice of voice database include /a/ and /m/. Second, we use Mel-Frequency Cepstral Coefficients (MFCC), Formant, Energy, to be different voice features. Third, we use two kinds of classfier. Including Gaussian mixture model (GMM) and Linear Discriminant Analysis (LDA). We use GMM to be acoustic model. LDA is exploited to increase the classification accuracy at a lower dimensional feature vector space. In our experiments, we found MFCC is the best voice feature. Because of MFCC outperforms Formant and Energy. The voice classification accuracy of female /a/ and /m/ are 84.5%. The voice classification accuracy of male /a/ is 69.73% and /m/ is 76.31%. If Lda is applied, MFCC can achieve classification accuracy as follows, The voice classification accuracy of female /a/ is 87.16% and /m/ is 86.8%. The voice classification accuracy of male /a/ is 73.11% and /m/ is 76.83%.
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author2 |
Ren-Yuan Lyu |
author_facet |
Ren-Yuan Lyu Liao wei chih 廖偉智 |
author |
Liao wei chih 廖偉智 |
spellingShingle |
Liao wei chih 廖偉智 The Study of Using Speech Recognition Technology to Help Diagnose for Rhinitis Patients |
author_sort |
Liao wei chih |
title |
The Study of Using Speech Recognition Technology to Help Diagnose for Rhinitis Patients |
title_short |
The Study of Using Speech Recognition Technology to Help Diagnose for Rhinitis Patients |
title_full |
The Study of Using Speech Recognition Technology to Help Diagnose for Rhinitis Patients |
title_fullStr |
The Study of Using Speech Recognition Technology to Help Diagnose for Rhinitis Patients |
title_full_unstemmed |
The Study of Using Speech Recognition Technology to Help Diagnose for Rhinitis Patients |
title_sort |
study of using speech recognition technology to help diagnose for rhinitis patients |
publishDate |
2006 |
url |
http://ndltd.ncl.edu.tw/handle/92767189670915068472 |
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