Time-Scale Feature Extractions for Emotional Speech Characterization

,Emotional,speech,characterization,is,an important issue for the understanding,of interaction. This article discusses the time-scale analysis problem in feature extraction for emotional speech processing. We describe a computational,framework,for combining,segmental,and supra-segmental features for emotional,speech detection. The statistical fusion is based on the estimation of local a posteriori class probabilities and the overall decision employs,weighting factors directly related to the duration of the individual speech segments. This strategy is applied to a real-world application: detection of Italian motherese in authentic and longitudinal parent–infant interaction at home. The results suggest that short- and long-term infor- mation, respectively, represented by the short-term spec- trum and the prosody parameters (fundamental frequency and,energy) provide,a robust and,efficient time-scale analysis. A similar fusion methodology,is also investigated by the use of a phonetic-specific characterization process. This strategy is motivated by the fact that there are varia- tions across emotional states at the phoneme,level. A time- scale based on both vowels and consonants is proposed and it provides a relevant and discriminant feature space for acted emotion recognition. The experimental results on two different databases Berlin (German) and Aholab (Basque) show,that the best performance,are obtained by our pho- neme-dependent,approach. These findings demonstrate the relevance of taking into account,phoneme,dependency (vowels/consonants) for emotional speech characterization.