Voice activity detection with low signal-to-noise ratio based on Hilbert-Huang transform

Voice activity detection plays an important role in speech signal processing.However,commonly used detection methods are not valid in low Signal-to-Noise Ratios(SNR),and the voice activity detection becomes difficult.Hilbert-Huang Transform(HHT) is a method designed for non-linear and non-stationary signals.Because of its adaptability,HHT can be used for analysis of available data.In this paper,the HHT is applied for voice activity detection under the condition of low SNR.This method finds the concentration component of the Intrinsic Mode Function(IMF) in the speech signal energy,analyzes the Hilbert spectrum by using the IMF and the Hilbert spectrum between pure voice signal and voice signal under low SNR.It can adaptively select threshold for voice and non-voice segment detection.Comparison of experiment results shows that this method is effective under low SNR of the detected speech signal.