关键词: 说话人识别, 马氏距离, 距离度量学习, 机器学习, 模式识别

Abstract: The i-vector speaker recognition systems usually compute the distance between the speaker utterances to determine their similarity. Weighted Pairwise Constraint Metric Learning algorithm（WPCML） trains a metric used to compute Mahalanobis distance by learning from the weighted constraints of pairwise training samples. In the sample space described by the metric, the distance between the same class samples is smaller, while that between the different class samples is larger. Experiment on the NIST 2008 speaker recognition data set demonstrates that the Mahalanobis distance scoring using the distance metric learned by WPCML algorithm has better performance than cosine distance scoring. It proposes a new way to construct the training pairwise set according to the Euclidean distance, which can improve the performance obviously and is better than the state-of-the-art PLDA classifier.

Key words: speaker recognition, Mahalanobis distance, distance metric learning, machine learning, pattern recognition