Proceedings of the International Conference on Advances in Mechanical Engineering and Industrial Informatics

Study on Optimization of Pathological Voice Features

Authors
Xiaojun Zhang, Yating Shao, Baoyin Sun, Zhi Tao
Corresponding Author
Xiaojun Zhang
Available Online April 2015.
DOI
10.2991/ameii-15.2015.123How to use a DOI?
Keywords
classification; optimal selection; pathological voice; recognition
Abstract

This paper proposes a method to optimize Pathological Voice Features based on classification and experimental design. Firstly, all voice parameters of features are classified, then pick the right amount of each class of representative and typical parameters based on the principle of mathematical statistics. Finally, recognition experiments are performed with this combination. C4.5 decision tree algorithm is used to recognize vocal cord or non-vocal cord damage voice, results show that recognition rate of optimized features is 5% higher than non-optimized, which is up to 89%.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the International Conference on Advances in Mechanical Engineering and Industrial Informatics
Series
Advances in Engineering Research
Publication Date
April 2015
ISBN
978-94-62520-69-1
ISSN
2352-5401
DOI
10.2991/ameii-15.2015.123How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xiaojun Zhang
AU  - Yating Shao
AU  - Baoyin Sun
AU  - Zhi Tao
PY  - 2015/04
DA  - 2015/04
TI  - Study on Optimization of Pathological Voice Features
BT  - Proceedings of the International Conference on Advances in Mechanical Engineering and Industrial Informatics
PB  - Atlantis Press
SP  - 665
EP  - 669
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-15.2015.123
DO  - 10.2991/ameii-15.2015.123
ID  - Zhang2015/04
ER  -