ITM Web Conf.
Volume 15, 2017II International Conference of Computational Methods in Engineering Science (CMES’17)
|Number of page(s)||6|
|Section||Computational And Artificial Intelligence|
|Published online||15 December 2017|
Kohonen network as a classifier of Polish emotional speech
Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Institute of Computer Science, Nadbystrzycka 38D, 20-618 Lublin, Poland
* Corresponding author: email@example.com
The power of speech is a main tool in human communication. There are a lot of factors as age, emotions, gender, pitch of the voice which can influence features of speech. Obviously, information conveyed by voice intonation has more than only textual meaning. The same sentence pronounced in two different ways can have two completely different meanings. This paper describes Kohonen networks as a classifier of Polish emotional speech. The usage of Discrete Wavelet Transform (DWT) as well as an innovative approach to scaleogram processing is also presented in this article. Mexican Hat Wavelet and the Haar Wavelet were used in researches. All simulations were carried out in MatLab 2016 with Neural Network Toolbar. During whole research more than 9000 simulation have been done. Three different speech databases were used in conducted researches. One of them was prepared by professional actors – four women and four men, and contains 240 wav files. Two others are results of researchers works. The structures of used Kohonen networks depend on speech signal decomposition’s level and scaleogram division. During conducted researches the following emotional states were considered: anger, joy, sadness, boredom, fear and neutral state. Achieved results were between 68% and 80% depends of used wavelet, speech signal and signal decomposition’s level.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.