Improved i-Vector Representation for Speaker Diarization

Abstract

This paper proposes using a previously well-trained deep neural network (DNN) to enhance the i-vector representation used for speaker diarization. In effect, we replace the Gaussian Mixture Model (GMM) typically used to train a Universal Background Model (UBM), with a DNN that has been trained using a different large scale dataset. To train the T-matrix we use a supervised UBM obtained from the DNN using filterbank input features to calculate the posterior information, and then MFCC features to train the UBM instead of a traditional unsupervised UBM derived from single features. Next we jointly use DNN and MFCC features to calculate the zeroth and first order Baum-Welch statistics for training an extractor from which we obtain the i-vector. The system will be shown to achieve a significant improvement on the NIST 2008 speaker recognition evaluation (SRE) telephone data task compared to state-of-the-art approaches