Speech Emotion Recognition by Using Combinations of Support Vector Machine (SVM), and C5.0
Speech Emotion Recognition by
Using Combinations of Support Vector Machine (SVM), and C5.0
Mohammad Masoud Javidi and Ebrahim Fazlizadeh
Roshan
Department of Computer Science,
Shahid Bahonar University of Kerman, Kerman, Iran.
ABSTRACT:
Speech emotion recognition
enables a computer system to records sounds and realizes the emotion of the
speaker. we are still far from having a natural interaction between the human
and machine because machines cannot distinguishes the emotion of the speaker. For
this reason it has been established a new investigation field, namely “the
speech emotion recognition systems”. The accuracy of these systems depend on
the various factors such as the type and the number of the emotion states and
also the classifier type. In this paper, the classification methods of C5.0,
Support Vector Machine (SVM), and the combination of C5.0 and SVM (SVM-C5.0)
are verified, and their efficiencies in speech emotion recognition are
compared. The utilized features in this research include energy, Zero Crossing
Rate (ZCR), pitch, and Mel-scale Frequency Cepstral Coefficients (MFCC). The
results of paper demonstrate that the effectiveness proposed SVM-C5.0
classification method is more efficient in recognizing the emotion of the
between -5.5 % and 8.9 % depending on the number of emotion states than SVM,
C5.0.
KEY WORDS:
Emotion recognition, Feature extraction,
Mel-scale Frequency Cepstral Coefficients, C5.0, Support Vector Machines
SOURCE URL
VOLUME LINK
.png)
Comments
Post a Comment