Advances in deep neural network approaches to speaker recognition

Abstract

The recent application of deep neural networks (DNN) to speaker identification (SID) has resulted in significant improvements over current state-of-the-art on telephone speech.  In this work, we report the same achievement in DNN-based SID performance on microphone speech.  We consider two approaches to DNN-based SID:  one that uses the DNN to extract features, and another that uses the DNN during feature modeling.  Modeling is conducted using the DNN/i-vector framework, in which the traditional universal background model is replaced with a DNN.  The recently proposed use of bottleneck features extracted from a DNN is also evaluated.  Systems are first compared with a conventional universal background model (UBM) Gaussian mixture model (GMM) i-vector system on the clean conditions of the NIST 2012 speaker  recognition evaluation corpus, where a lack of robustness to microphone speech is found.  Several methods of DNN feature processing are then applied to bring significantly greater robustness to microphone speech.  To direct future research, the DNN-based systems are also evaluated in the context of audio degradations including noise and reverberation.