Adaptive thresholding approach for robust voiced/unvoiced classification

This paper presents a robust voiced/unvoiced classification method by using linear model of empirical mode decomposition (EMD) controlled by Hurst exponent. EMD decomposes any signals into a finite number of band limited signals called intrinsic mode functions (IMFs). It is assumed that voiced speech signal is composed of trend due to vocal cord vibration and some noise. No trend is present in unvoiced speech signal. A linear model is developed using IMFs of the noise part of the speech signal. Then a specified confidence interval of the linear model is set as the data adaptive energy threshold. If there exists at least one IMF exceeding the threshold and its fundamental period is within the pitch range, the speech is classified as voiced and unvoiced otherwise. The experimental results show that the proposed method performs superior compared to the recently developed voiced/unvoiced classification algorithms with noticeable performance.