  Emotional state recognition through speech is being a very interesting
research topic nowadays. Using subliminal information of speech, denominated as
prosody, it is possible to recognize the emotional state of the person. One of
the main problems in the design of automatic emotion recognition systems is the
small number of available patterns. This fact makes the learning process more
difficult, due to the generalization problems that arise under these
conditions. In this work we propose a solution to this problem consisting in
enlarging the training set through the creation the new virtual patterns. In
the case of emotional speech, most of the emotional information is included in
speed and pitch variations. So, a change in the average pitch that does not
modify neither the speed nor the pitch variations does not affect the expressed
emotion. Thus, we use this prior information in order to create new patterns
applying a gender dependent pitch shift modification in the feature extraction
process of the classification system. For this purpose, we propose a frequency
scaling modification of the Mel Frequency Cepstral Coefficients, used to
classify the emotion. For this purpose, we propose a gender dependent frequency
scaling modification. This proposed process allows us to synthetically increase
the number of available patterns in the training set, thus increasing the
generalization capability of the system and reducing the test error. Results
carried out with two different classifiers with different degree of
generalization capability demonstrate the suitability of the proposal.
