@inproceedings{1bb4ab24f290409da499943c710508b7,
title = "Speech emotion recognition based on entropy of enhanced wavelet coefficients",
abstract = "This paper presents a speaker-independent speech emotion recognition method, where emotional features are derived from the Teager energy (TE) operated wavelet coefficients of speech signal. Due to TE operation, the enhanced detail as well as approximate Wavelet coefficients thus obtained is then used to compute entropy. Entropy values of TE operated detail and approximate wavelet coefficients not only reduces feature dimension but also form an effective feature vector for distinguishing different emotions when fed to a Euclidean distance based classifier. Extensive simulations are carried out using EMO-DB German speech emotion database containing four class emotions, such as angry, happy, sad and neutral. Simulation results show that the proposed method is capable of outperforming an existing speaker-independent emotion recognition method thus solving a four-class emotion recognition problem in terms of higher recognition accuracy with lower computation.",
keywords = "Entropy, Euclidean Distance, Speaker-independent, Teager Energy, Wavelet",
author = "S. Sultana and C. Shahnaz and Fattah, {S. A.} and I. Ahmmed and Zhu, {W. P.} and Ahmad, {M. O.}",
year = "2014",
doi = "10.1109/ISCAS.2014.6865084",
language = "English (US)",
isbn = "9781479934324",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "137--140",
booktitle = "2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014",
address = "United States",
note = "2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014 ; Conference date: 01-06-2014 Through 05-06-2014",
}