A model-based voice Activity Detection algorithm using probabilistic neural networks

In this paper we introduce an efficient probabilistic neural networks (PNN) model-based voice activity detection (VAD) algorithm. The inputs for PNN are code excited linear prediction coder parameters, which are stable under background noise. The PNN network output is 1 or 0 to determine the nature of the period (speech or NonSpeech). Experimental results show that the proposed VAD algorithm achieves better performance than G.729 Annex B at any noise level. The performance compares very favorably with Adaptive MultiRate VAD, phase 2 (AMR2).