Emotional speech feature normalization and recognition based on speaker sensitive feature clustering
In this paper we propose a feature normalization method for speaker independent speech emotion recognition. The performance of a speech emotion classifier largely depends on the training data, and a large number of unknown speakers may cause a great challenge. To address this problem, first, we extract and analyse 481 basic acoustic features. Second, we use principal component analysis and linear discriminant analysis jointly to construct the speaker sensitive feature space. Third, we classify the emotional utterances into pseudo speaker groups in the speaker sensitive feature space by using fuzzy k means clustering. Finally, we normalize the original basic acoustic features of each utterance based on its group information. To verify our normalization algorithm, we adopt a Gaussian mixture model based classifier for recognition test. The experimental results show that our normalization algorithm is effective on our locally collected database, as well as on the eNTERFACE'05 Audio Visual Emotion Database. The emotional features achieved using our method are robust to the speaker change, and an improved recognition rate is observed.
Int. J. Speech Technol.