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  • 标题:Time-Domain Features And Probabilistic Neural Network For The Detection Of Vocal Fold Pathology
  • 本地全文:下载
  • 作者:M. Hariharan ; M. P. Paulraj ; Sazali Yaacob
  • 期刊名称:Malaysian Journal of Computer Science
  • 印刷版ISSN:0127-9084
  • 出版年度:2010
  • 卷号:23
  • 期号:1
  • 出版社:University of Malaya * Faculty of Computer Science and Information Technology
  • 摘要:Due to the nature of job, unhealthy social habits and voice abuse, people are subjected to the risk of voice problems. It is well known that most of vocal fold pathologies cause changes in the acoustic voice signal. Therefore, the voice signal can be a useful tool to diagnose them. Acoustic voice analysis can be used to characterize the pathological voices. This paper presents the detection of vocal fold pathology with the aid of the speech signal recorded from the patients. The speech samples from Massachusetts Eye and Ear Infirmary (MEEI) database are used to evaluate the scheme. Timedomain features based on energy variation are proposed and extracted from the speech to form a feature vector. In order to test the effectiveness and reliability of the proposed timedomain features, a Probabilistic Neural Network (PNN) is employed. The experimental results show that the proposed features gives very promising classification accuracy and can be effectively used to detect the vocal fold pathology clinically.
  • 关键词:Acoustic Analysis; Vocal Fold Pathology; TimeDomain Features; Probabilistic Neural Network
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