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Statistical Analysis for Database Dependence in Classification of Emotional Speech by using Different Features Extraction Approaches
SUN Ying;ZHANG Xue-ying
2011, 31 (4):
132-136.
DOI: 10.3969/j.issn.1006-1355-2011.04.031
Four approaches of feature extraction: the Linear Predictive Cepstral Coefficient (LPCC), the Teager Energy Operator (TEO), the Mel-Frequency Cepstral Coefficient (MFCC) and the Zero Crossings with Peak Amplitudes (ZCPA) are described in this paper. And these approaches are applied to emotional speech recognition. Two kinds of experiments are carried out. The first one is a kind of single language experiments with TYUT database and Berlin database. Its results show that these four approaches can represent speech emotion effectively by using single language of single database. MFCC has the best result of the four approaches. The second kind experiment is merge-database of single language. Most previous work on emotional feature extraction is based on a special language of single speech database. But in practice, the environment of the speaker is various. So the study of emotional feature extraction based on merge-database is significative. Experiments of the second kind indicate that the four features are all database dependent. ZCPA features are of the least database dependence of the four approaches.
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