Convolutional Neural Networks for Pathological Voice Detection

被引：0

作者：

Wu, Huiyi ^{[1
]}

Soraghan, John ^{[1
]}

Lowit, Anja ^{[2
]}

Di Caterina, Gaetano ^{[1
]}

机构：

[1] Univ Strathclyde, Ctr Signal & Image Proc, Dept Elect & Elect Engn, Glasgow G1 1XW, Lanark, Scotland

[2] Univ Strathclyde, Sch Psychol Sci & Hlth, Speech & Language Therapy, Glasgow G1 1QE, Lanark, Scotland

来源：

2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2018年

关键词：

AUTOMATIC DETECTION;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Acoustic analysis using signal processing tools can be used to extract voice features to distinguish whether a voice is pathological or healthy. The proposed work uses spectrogram of voice recordings from a voice database as the input to a Convolutional Neural Network (CNN) for automatic feature extraction and classification of disordered and normal voice. The novel classifier achieved 88.5%, 66.2% and 77.0% accuracy on training, validation and testing data set respectively on 482 normal and 482 organic dysphonia speech files. It reveals that the proposed novel algorithm on the Saarbruecken Voice Database can effectively been used for screening pathological voice recordings.

引用

页码：4784 / 4787

页数：4

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