Previous feature mapping needs a lot of corpus with channel flags. Recently unsupervised clustering on channels also needs a series of speech recorded under different channels. This paper discusses a new speaker verification method based on supervector clustering, in order to ensure the performance and reduce the data requirements. An approach based on supervector clustering under poor training corpus using the inter-speaker variability between male and female is presented. Mixed effects of speaker and channel information are clustered, then after the decision on categories of unprocessed speech feature mapping is conducted. Experiments show advantages compared with other methods under poor corpus，from corpus and performance perspective.