The “UTDrive” in-vehicle voice activity detection system

Abstract

In this study, we specifically address the problem of in-vehicle voice activity detection (VAD), which has a significant importance for the speech controlled intelligent vehicle. A novel VAD system is proposed based on microphone array beam- forming and discriminative Gaussian mixture model. As a binary classification problem, the features and classifiers are explored under the in-vehicle acoustic environment. Using microphone array, we show that the spatial power ratio can serve as an effective feature for speech activity detection. Further, a discriminative training based Gaussian mixture model (GMM) classifier is employed to enhance the VAD performance in terms of receiver operating characteristics (ROC). Compared to the conventional VAD systems, the proposed VAD system presents a novel and robust performance against various in-vehicle noisy scenarios from the UTDrive project.