Frame-level features conveying phonetic information for language and speaker recognition

150 p.

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.

"Erran nahi baita" birformulatzaile esplikatiboa gaurko euskaran

[EN] This article presents a detailed study of the present-day use of the Basque discourse marker "erran/esan nahi baita" (‘that is to say’). This is an explanatory reformulator by means of which the speaker presents a reformulation of something said in the previous utterance (either a clause that forms part of the current sentence, or the preceding sentence) in order to express it more clearly or explain it. In the article I will examine the marker’s values; literary tradition; form and origin; present-day variants; equivalent expressions; position; punctuation; syntax; frequency, medium (written or spoken), register and text type; equivalents in other languages; and discourse value.