Emulating human cognitive approach for speech emotion recognition using MLP and GenSoFNN
Speech emotion recognition field is growing due to the increasing needs for effective human-computer interaction. There are many approaches in term of features extraction methods coupled with classifiers to obtain optimum performance. However, none can claim superiority as it is very data-depen...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/31010/ http://irep.iium.edu.my/31010/ http://irep.iium.edu.my/31010/1/Table_of_Content.pdf http://irep.iium.edu.my/31010/2/106.pdf |
| Summary: | Speech emotion recognition field is growing due to
the increasing needs for effective human-computer interaction.
There are many approaches in term of features extraction
methods coupled with classifiers to obtain optimum
performance. However, none can claim superiority as it is very
data-dependant and domain oriented. In this paper, the
appropriate sets of features are investigated using segregation
method and feature ranking algorithm of Automatic Relevance
Determination (ARD) [1]. Two popular classifiers of Multi
Layer Perceptron (MLP) [2] and Generic Self-organizing Fuzzy
Neural Network (GenSoFNN) [3] are employed to discriminate
emotions in the data corpus used in the FAU Aibo Emotion
Corpus [4, 5]. The experimental results shows that Mel
Frequency Cepstral Coefficient (MFCC) [6] features are able to
yield comparable accuracy with baseline result [5]. In addition,
it is observed that MLP can perform slightly better than
GenSoFNN. Hence, such system envisages that appropriate
combination of features extracted with good classifiers is
fundamental for the good speech emotion recognition system. |
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