19 July 2013 Study of wavelet packet energy entropy for emotion classification in speech and glottal signals
Author Affiliations +
Proceedings Volume 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013); 887834 (2013) https://doi.org/10.1117/12.2030929
Event: Fifth International Conference on Digital Image Processing, 2013, Beijing, China
Abstract
The automatic speech emotion recognition has important applications in human-machine communication. Majority of current research in this area is focused on finding optimal feature parameters. In recent studies, several glottal features were examined as potential cues for emotion differentiation. In this study, a new type of feature parameter is proposed, which calculates energy entropy on values within selected Wavelet Packet frequency bands. The modeling and classification tasks are conducted using the classical GMM algorithm. The experiments use two data sets: the Speech Under Simulated Emotion (SUSE) data set annotated with three different emotions (angry, neutral and soft) and Berlin Emotional Speech (BES) database annotated with seven different emotions (angry, bored, disgust, fear, happy, sad and neutral). The average classification accuracy achieved for the SUSE data (74%-76%) is significantly higher than the accuracy achieved for the BES data (51%-54%). In both cases, the accuracy was significantly higher than the respective random guessing levels (33% for SUSE and 14.3% for BES).
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ling He, Ling He, Margaret Lech, Margaret Lech, Jing Zhang, Jing Zhang, Xiaomei Ren, Xiaomei Ren, Lihua Deng, Lihua Deng, } "Study of wavelet packet energy entropy for emotion classification in speech and glottal signals", Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 887834 (19 July 2013); doi: 10.1117/12.2030929; https://doi.org/10.1117/12.2030929
PROCEEDINGS
6 PAGES


SHARE
Back to Top