Enhanced SVM Training for Robust Speech Activity Detection

Speech activity detection (SAD) is a key objective in speech-related technologies. In this work, an enhanced version of the training stage of a SAD system based on a support vector machine (SVM) classifier is presented, and its performance is tested with the RT05 and RT06 evaluation tasks. A fast algorithm of data reduction based on proximal SVM has been developed and, furthermore, the specific characteristics of the metric used in the NIST SAD evaluation have been taken into account during training. Tested with the RT06 data, the resulting SVM SAD system has shown better scores than the best GMM-based system developed by the authors and submitted to the past RT06 evaluation.