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...

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Bibliographic Details
Main Authors: Farsinejad, M., Mohammadi, M., Nasersharif, B., Akbari, A.
Format: Conference Proceeding
Language:English
Subjects:
Background noise
Computer networks
Detection algorithms
Mathematical model
MATLAB
Neural networks
Neurons
Noise level
PNN-VAD
probabilistic neural networks
Speech coding
Transfer functions
Voice activity detection
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Summary: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).
ISSN:2163-0771