As an effective and low-dimension representation for speech utterances with different lengths,i-vector method has drawn considerable attentions in speaker verification. Training a Total variability space（TVS） is one of the key parts in the i-vector method. However, the traditional training method only explores the relationship between different mean supervectors, ignoring priori category information of speakers, which results in a lack of discrimination. In the proposed method, a discriminative TVS based on Partial least squares（PLS） is estimated, in which both the correlation of intra-class and the distinction of inter-class are fully utilized due to using speaker labels, and the proposed method can achieve a better performance.
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