Noise-Robust Algorithm for "Speech/Pause" Segmentation in Diagnostic Systems of Psychogenic States

Abstract

Low detection accuracy of speech signal boundaries and pauses is one of the main problems of practical realization of diagnostic systems of psychogenic states. This paper proposes a noise-robust algorithm for 'speech/pause' segmentation, operating under free physical activity of a patient. In developing the algorithm the following methods were used: a method for adaptive processing of non-stationary signals – the Complementary Ensemble Empirical Mode Decomposition (CEEMD), a statistical data processing method – the Independent Component Analysis (ICA), a differentiation method using the concepts of normal distribution and one-dimensional Mahalanobis distance. The article presents a block diagram for the algorithm with a detailed mathematical description. The advantages over the known 'speech/pause' segmentation algorithms are shown. The developed algorithm enhances the actual detection rate by the average of 11.3%. A comparison of researches' results suggests that the developed 'speech/pause' segmentation algorithm is recommended for practical application in the diagnostic systems of psychogenic states, operating under free physical activity of a patient.