Abstract
This paper is dedicated to the description and the study of a new feature extraction approach for emotion recognition. Our contribution is based on the extraction and the characterization of phonemic units such as vowels and consonants, which are provided by a pseudo-phonetic speech segmentation phase combined with a vowel detector. The segmentation algorithm is evaluated on both emotional (Berlin) and non-emotional (TIMIT, NTIMIT) databases. Concerning the emotion recognition task, we propose to extract MFCC acoustic features from these pseudo-phonetic segments (vowels, consonants) and we compare this approach with traditional voice and unvoiced segments. The classification is achieved by the well-known k-nn classifier (k nearest neighbors) on the Berlin corpus.
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Ringeval, F., Chetouani, M. (2008). Exploiting a Vowel Based Approach for Acted Emotion Recognition. In: Esposito, A., Bourbakis, N.G., Avouris, N., Hatzilygeroudis, I. (eds) Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction. Lecture Notes in Computer Science(), vol 5042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70872-8_19
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DOI: https://doi.org/10.1007/978-3-540-70872-8_19
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