Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

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 selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

A compact diode-pumped injection seeded Nd : YAG laser with resonance-detection technique

A diode pumped injection seeded single-longitudinal-mode (SLM) Nd:YAG laser is achieved by using the resonance-detection technique in Q-switching operation. The pulsed oscillator laser uses a folded cavity to achieve compact construction. This system operates at 100 Hz and provides over 20 mJ/pulse of single-frequency 1064 nm output. The M-2 values of horizontal and vertical axes are 1.58 and 1.41, respectively. The probability of putting out single-longitudinal-mode pulses is 100%. The 355 nm laser output produced by frequency tripling has a linewidth less than 200 MHz. The laser can run over eight hours continually without mode hopping.