Emotion recognition from speech signal using fuzzy clustering

Expressive speech modeling is a new trend in speech processing, including emotional speech synthesis and recognition. So far, emotion recognition from speech signal has been mainly achieved using supervised classifiers. However, clustering techniques seem well fitted to resolve such a problem, especially in huge databases, where speech labeling may be a hard and tedious task. This paper presents a novel approach for emotion recognition from speech signal, based on fuzzy clustering, including probabilistic, possibilistic and graded-possibilistic c-means. In comparison to crisp clustering, mainly using kmeans, fuzzy c-means look more fitted for this problem, and potentially offer an innovative way to analyze emotions conveyed by speech using membership degrees.