A simple but efficient real-time Voice Activity Detection algorithm

Voice Activity Detection (VAD) is a very important front end processing in all Speech and Audio processing applications. The performance of most if not all speech/audio processing methods is crucially dependent on the performance of Voice Activity Detection. An ideal voice activity detector needs to be independent from application area and noise condition and have the least parameter tuning in real applications. In this paper a nearly ideal VAD algorithm is proposed which is both easy-to-implement and noise robust, comparing to some previous methods. The proposed method uses short-term features such as Spectral Flatness (SF) and Short-term Energy. This helps the method to be appropriate for online processing tasks. The proposed method was evaluated on several speech corpora with additive noise and is compared with some of the most recent proposed algorithms. The experiments show satisfactory performance in various noise conditions.