Abstract
In this paper we analyze the separability and recognition of emotion states in multilingual speech signals statistically. Prosodic features such as pitch, energy and time parameters are extracted and the separability is discussed based on the statistical results in a two-dimension method. Principal component analysis (PCA) is then used to recognize emotion states and achieved satisfying results, in which mean recognition rate is 72.26% and the highest recognition rate is 89%. The results show that language factors do not affect features of prosodic variation of some given emotion obviously and basic emotions can be recognized roughly from speech signals using prosodic parameters.
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