Speech emotion recognition
Minimum feature
extraction
ZCR
12 MFCC
Random forest
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Recognizing speech emotions is an important
subject in pattern recognition. This work is about studying the
effect of extracting the minimum possible number of features
on the speech emotion recognition (SER) system. In this paper,
three experiments performed to reach the best way that gives
good accuracy. The first one extracting only three features:
zero crossing rate (ZCR), mean, and standard deviation (SD)
from emotional speech samples, the second one extracting only
the first 12 Mel frequency cepstral coefficient (MFCC)
features, and the last experiment applying feature fusion
between the mentioned features. In all experiments, the
features are classified using five types of classification
techniques, which are the Random Forest (RF),
...
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