Feature selection for speech emotion recognition in Spanish and Basque: on the use of machine learning to improve human-computer interaction.

ABSTRACT: Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset

SUBMITTER: Arruti A 

PROVIDER: S-EPMC4184843 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature