Large margin nearest neighborhood metric learning for i-vector based speaker verification

A new large margin nearest neighborhood metric learning (LMNN) method for i-vector based speaker verification is proposed in this paper. In general, a verification decision is taken by computing the cosine distance between the i-vectors of the test utterance and the claimed identity. LMNN metric is learned from the examples and can be viewed as a linear transformation of the input i-vector space of the training and test utterance. In this work, the metric is learned with the objective of reducing the distance between the i-vectors of same class of speaker, while impostors are separated by a large margin. The metric learned in this manner leads to a better speaker verification performance. Speaker verification experiments are then conducted on the NIST 2008 and YOHO speaker verification databases. Experimental results indicate a reasonable improvement in performance, when compared to i-vector based speaker verification methods which use conventional cosine scoring.