Voice Activity Detection Using Neurograms

Abstract

Existing acoustic-signal-based algorithms for Voice Activity Detection (VAD) do not perform well in the presence of noise. In this study, we propose a method to improve VAD accuracy by employing another type of signal representation which is derived from the response of the human Auditory-Nerve (AN) system. The neural responses referred to as a neurogram are simulated using a computational model of the AN system for a range of Characteristic Frequencies (CFs). Features are extracted from neurograms using the Discrete Cosine Transform (DCT), and are then trained using a Multilayer Perceptron (MLP) classifier to predict the VAD intervals. The proposed method was evaluated using the QUT-NOISE-TIMIT corpus, and the NIST scoring algorithm for VAD was employed as an accuracy measure. The proposed neural-response-based method exhibited an overall better VAD accuracy over most of the existing methods.