Performance Analysis of Voice Activity Detection Algorithms for Robust Speech Recognition

The emerging applications of speech technology especially in the fields of wireless applications, digital hearing aids or speech recognition are often requiring a noise reduction technique in combination with a precise Voice Activity Detector (VAD). In this paper, we compare the performance of the VAD algorithms like Zero Crossing Detection(ZCD), Weak Fricative Detection (WFD), Pitch Based Detection (PBD), Energy Based Detection (EBD) and Subband Order Statistics Filter (OSF) in presence of different types of noise like airport, babble, train, car, street, exhibition, restaurant and leopard for Automatic Speech Recognition (ASR). When analysis was done under various noise conditions for speech recognition, it was found that Subband Order statistics Filter (OSF) method algorithm performs better than other VAD algorithms.