Exploiting a Vowel Based Approach for Acted Emotion Recognition

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.

• Type: Conference
• Year: 2007
• Where: COST
• Authors: Fabien Ringeval, Mohamed Chetouani
• Keywords: COST 2007, Emotion Recognition, Feature Extraction Approach, Speech Segmentation Phase