Cosine Distance Metric Learning for Speaker Verification Using Large Margin Nearest Neighbor Method
In this paper, a novel cosine similarity metric learning based on large margin nearest neighborhood LMNN is proposed for an i vector based speaker verification system. Generally, in an i vector based speaker verification system, the decision is based on the cosine distance between the test i vector and target i vector. Metric learning methods are employed to reduce the within class variation and maximize the between class variation. In this proposed method, cosine similarity large margin nearest neighborhood CSLMNN metric is learned from the development data. The test and target i vectors are linearly transformed using the learned metric. The objective of learning the metric is to ensure that the k nearest neighbors that belong to the same speaker are clustered together, while impostors are moved away by a large margin. Experiments conducted on the NIST 2008 and YOHO databases show improved performance compared to speaker verification system, where no learned metric is used.
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