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In the field of speaker recognition, the Gaussian mixture model with diagonal covariance matrices is a popular technique, in this way, it simplified model and reduced the amount of computation, but lost the correlation information between feature vectors, and then influenced the classification performance. In this paper, in order to compensate the correlation between feature elements, we proposed...
Speech with various emotions aggravates the performance of speaker recognition system. The existing speaker modeling disregards the match of the emotional state between training and testing speech, and the systems suffer the lapsus of the emotion recognition as to practical application. We propose an alternative approach that exploits the prosodic difference to cluster affective speech, and then builds...
We investigate the symmetric Kullback-Leibler (KL2) distance in speaker clustering and its unreported effects for differently-sized feature matrices. Speaker data is represented as Mel frequency cepstral coefficient (MFCC) vectors, and features are compared using the KL2 metric to form clusters of speech segments for each speaker. We make two observations with respect to clustering based on KL2: 1...
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