Spectral energy based voice activity detection for real-time voice interface

Voice activity detection (VAD) is a main process of speech recognition tasks in which every voice region is detected to extract acoustic feature parameters from the region. This paper proposes an efficient VAD approach for applying to real-time voice interface systems. Even though diverse VAD approaches have been successfully applied for speech applications, they may operate inefficiently according to environmental conditions. In this study, we attempt to enhance the conventional VAD method based on signal energy within time and spectral domain. In addition, an efficient end-point detection method is also proposed. We successfully verified the efficiency of the proposed approach via a set of VAD experiments, comparing with the performance of some conventional VAD methods including zero crossing rate.