Abstract Speech with various emotions aggravates the performance of speaker recognition system. A pitch-dependent affective speech clustering method for speaker modeling is proposed. This method aims to exploiting the affective material effectively in the speaker systems. Thresholds for pitches are determined for the male and the female separately. The cepstral features in the same pitch range are clustered. Different pitch-dependent models are built with the corresponding cluster features by map adaptation for each speaker. The maximum likelihood rule is applied to the matched models and the identification of the person. The proposed method is evaluated on the mandarin affective speech corpus. Experimental results show that the proposed approach is more powerful and efficient than the cepstral feature based method and the structure training method for speaker recognition.
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2009, Vol. 22 
