Speaker state recognition with neural network-based classification and self-adaptive heuristic feature selection

While the implementation of existing feature sets and methods for automatic speaker state analysis has already achieved reasonable results, there is still much to be done for further improvement. In our research, we tried to carry out speech analysis with the self-adaptive multi-objective genetic algorithm as a feature selection technique and with a neural network as a classifier. The proposed approach was evaluated using a number of multi-language speech databases (English, German and Japanese). According to the obtained results, the developed technique allows an increase in emotion recognition performance by up to 6.2% relative improvement in average F-measure, up to 112.0% for the speaker identification task and up to 6.4% for the speech-based gender recognition, having approximately half as many features.