Non-negative matrix factorization based discriminative features for speaker verification

Discovering a discriminative feature representative together with a suitable distance measure is the key for a successful speaker recognition system. In this paper, we propose a new approach for automatic speaker verification. The main contribution of the paper is the extraction of discriminative speaker features using non-negative matrix factorization (NMF) decomposition in the GMM mean space, and the use of cosine-distance measure for speaker classification. With the decomposition, the speaker space is represented by the pattern components while a speaker can be characterized by a coefficient vector representing a specific localization in the space. We validate the proposed approach on the 10-second training and 10-second testing condition constructed from 863 Putonghua (Mandarin) corpus. Relative 10.57% and 26.11% improvements compared to the conventional GMM-UBM system have been achieved for female and male trials respectively.