Parametric cepstral analysis for pathological voice assessment

Abstract

Traditional methods to diagnose laryngeal pathologies such as laryngoscopy are considered invasive and uncomfortable. Methods based on acoustic analisys of speech signals have been investigated in order to diminish the number of laryngoscopical exams. Digital signal processing techniques have been used to perform an acoustic analysis for vocal quality assessment due to the simplicity and the non-invasive nature of the measurement procedures. Their employment is of special interest, as they can provide an objective diagnosis of pathological voices, and may be used as complementary tool in laryngoscopy. The degree of reliability and effectiveness of discriminating process of pathological voices from normal ones depends on the characteristics and parameters of voice used to train the employed classifier. This paper aims at evaluating the performance of the Linear Prediction Coding (LPC)-based cepstral analysis to discriminate pathological voices of speakers affected by vocal fold edema. For this purpose, LPC, cepstral, weighted cepstral, delta cepstral weighted delta cepstral mel-cepstral coefficients and are used. A vector-quantizing-trained distance classifier is used in the discrimination process.