AUC Optimization for Deep Learning Based Voice Activity Detection

Voice activity detection (VAD) based on deep neural networks (DNN) has demonstrated good performance in adverse acoustic environments. Current DNN based VAD optimizes a surrogate function, e.g. minimum cross-entropy or minimum squared error, at a given decision threshold. However, VAD usually works on-the-fly with a dynamic decision threshold; and ROC curve is a global evaluation metric of VAD that reflects the performance of VAD at all possible decision thresholds. In this paper, we propose to optimize the area under ROC curve (AUC) by DNN, which can maximize the performance of VAD in terms of the ROC curve. Experimental results show that optimizing AUC by DNN results in higher performance than the common method of optimizing the minimum squared error by DNN.