Robust speaker recognition based on DNN/i-vectors and speech separation

Abstract

Recent research shows that the i-vector framework for speaker recognition can significantly benefit from phonetic information. A common approach is to use a deep neural network (DNN) trained for automatic speech recognition to generate a universal background model (UBM). Studies in this area have been done in relatively clean conditions. However, strong background noise is known to severely reduce speaker recognition performance. This study investigates a phonetically-aware i-vector system in noisy conditions. We propose a front-end to tackle the noise problem by performing speech separation and examine its performance for both verification and identification tasks. The proposed separation system trains a DNN to estimate the ideal ratio mask of the noisy speech. The separated speech is then used to extract enhanced features for the i-vector framework. We compare the proposed system against a multi-condition trained baseline and a traditional GMM-UBM i-vector system. Our proposed system provides an absolute average improvement of 8% in identification accuracy and 1.2% in equal error rate.