Discriminative Training for Multiple Observation Likelihood Ratio Based Voice Activity Detection

It is possible to show that the likelihood ratio (LR) test from multiple observations can enhance the performance of a statically modeled voice actively detection (VAD) system. However, the combination weights for the likelihood ratios (LRs) in each observation are rather empirical and heuristical. In this study, the optimal combination weights from two discriminative training methods are studied to directly improve VAD performance, in terms of reduced misclassification errors and improved receiver operating characteristics (ROC) curves. As shown in the evaluations, VAD performance, both in terms of absolute performance and consistency across noise types, can be significantly improved using the proposed method.