@article{(Open Science Index):https://publications.waset.org/pdf/9314,
	  title     = {Applications of Support Vector Machines on Smart Phone Systems for Emotional Speech Recognition},
	  author    = {Wernhuar Tarng and  Yuan-Yuan Chen and  Chien-Lung Li and  Kun-Rong Hsie and  Mingteh Chen},
	  country	= {},
	  institution	= {},
	  abstract     = {An emotional speech recognition system for the
applications on smart phones was proposed in this study to combine
with 3G mobile communications and social networks to provide users
and their groups with more interaction and care. This study developed
a mechanism using the support vector machines (SVM) to recognize
the emotions of speech such as happiness, anger, sadness and normal.
The mechanism uses a hierarchical classifier to adjust the weights of
acoustic features and divides various parameters into the categories of
energy and frequency for training. In this study, 28 commonly used
acoustic features including pitch and volume were proposed for
training. In addition, a time-frequency parameter obtained by
continuous wavelet transforms was also used to identify the accent and
intonation in a sentence during the recognition process. The Berlin
Database of Emotional Speech was used by dividing the speech into
male and female data sets for training. According to the experimental
results, the accuracies of male and female test sets were increased by
4.6% and 5.2% respectively after using the time-frequency parameter
for classifying happy and angry emotions. For the classification of all
emotions, the average accuracy, including male and female data, was
63.5% for the test set and 90.9% for the whole data set.},
	    journal   = {International Journal of Electronics and Communication Engineering},
	  volume    = {4},
	  number    = {12},
	  year      = {2010},
	  pages     = {1730 - 1737},
	  ee        = {https://publications.waset.org/pdf/9314},
	  url   	= {https://publications.waset.org/vol/48},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 48, 2010},
	}