Improved speaker recognition system for stressed speech using deep neural networks

Abstract

Good speaker recognition systems should identify the speaker irrespective of what is spoken, including non-speech sounds that are often produced during natural conversations. In this work, the inclusion of breath sounds in the training phase of the speaker recognition is analyzed using the popular Gaussian mixture model-universal background model (GMM-UBM) and deep neural network (DNN) based systems. It is shown that the DNN-based systems have a better learning capability to perform well even on unseen data compared to GMM-UBM-based systems. Specifically, enhancement in speaker recognition performance is obtained on unseen stressed speech data by training systems with both breath sounds and modal speech. Experimental results show that inclusion of breath sounds in training data reduces the equal error rate (EER) of the speaker recognition system on stressed speech by 40% to 50% in absolute terms. It is also shown that increasing the number of hidden layers help DNNs to improve the performance even on unseen data.