Employing linear prediction residual signal of wavelet sub-bands in automatic detection of laryngeal pathology

Abstract

An automatic voice pathology detection scheme based on the processing of speech signal is introduced that is highly reliable in detecting various vocal folds impairments. Using linear prediction (LP) analysis to accurately keep track the variations of glottal signal from speech signal is the key element of our proposed method. The linear prediction-based residual signal estimation is employed to monitor the irregularity of glottal signal with the aim of providing information about vocal folds status. The procedure is done by decomposing a voiced signal (/a/) selected from Kay Elemetrics databases using 1-D discrete wavelet decomposition in four levels, and then applying linear prediction (LP) analysis on achieved coefficients of wavelet sub-band to capture a time-frequency representation of vocal folds vibrations. Support vector machine is finally used to make a decision about the existence of any abnormality in the vocal folds of the analyzed sample. Experimental results indicate that the extracted residual signals from wavelet sub-bands provide highly reliable features especially where there are a variety of abnormal voices that are applicable for assessing voice quality and the effectiveness of the prescribed rehabilitation medicine.