Exploiting a Vowel Based Approach for Acted Emotion Recognition

This paper is devoted to the description of a new approach for emotion recognition. Our contribution is based on both the extraction and the characterization of phonemic units such as vowels and consonants, which are provided by a pseudophonetic speech segmentation phase combined with a vowel detector. Concerning the emotion recognition task, we explore acoustic and prosodic features from these pseudo-phonetic segments (vowels and consonants), and we compare this approach with traditional voiced and unvoiced segments. The classification is realized by the well-known k-nn classifier (k nearest neighbors) from two different emotional speech databases: Berlin (German) and Aholab (Basque).

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