Correlation coefficient-based voice activity detector algorithm

A voice activity detector (VAD) is an algorithm able to distinguish the speech regions from the background noise of the input signal and is an important step in many speech processing applications. The varying nature and the large variety of speech and background noise make this problem difficult especially for low signal to noise ratio (SNR) that is the case for many practical applications. In this paper we propose a new VAD algorithm designed to improve the solution of word boundary detection problem for variable background noise level in a real time application. The input signal is windowed in time domain and then the energy and the spectrum of the current frame are obtained. The first few frames are supposed not to contain speech and are used to obtain a first estimate of the noise parameters. These parameters are updated during the silence periods using a first order autoregressive filter. In order to obtain robust parameters that do not depend on the amplitude of the spectrum, the correlation coefficient of the instantaneous spectrum and an average of the background noise spectrum is calculated. The speech regions may be detected based on a statistical approach using a simple binary Markov model for speech activity process. To evaluate the performance of the proposed method a clean speech dataset from the TIMIT database corrupted with different types of noise from NOISEX database for different SNR levels has been utilized.