Voice activity detection in degraded speech using excitation source information

This paper proposes a method for detection of voiced regions from speech signals collected in noisy environment. The proposed method is based on the characteristics of excitation source of speech production. The degraded speech signal is processed by linear prediction analysis for deriving the linear prediction residual. Hilbert envelope of the linear prediction residual is processed using covariance analysis to obtain coherently-added covariance signal. The periodicity property of the coherently added covariance signal is exploited to detect the voiced regions using autocorrelation analysis. The performance of the proposed voice activity detection algorithm is evaluated under different noise environments and at different levels of degradation.