Pathological assessment of patients speech signals using nonlinear dynamical analysis
Acoustic analysis of voice features can complete the invasive observation-based methods for the diagnosis of vocal fold pathologies. Selection of an appropriate feature extraction method from the voice can significantly improve the diagnostic results for patients with vocal disorders. In this paper, the performance of nonlinear dynamics and acoustical perturbation features is evaluated in order to distinguish patients with vocal fold disorder and other normal cases. As a matter of fact, vocal fold pathology is one of the major causes of voice quality reduction or feature variation in patients with dysphonic voices. Due to the devastating impact of vocal folds dysfunction on the complex dynamical structure of the speech signals, spectral analysis methods are not suitable for characterizing such changes in disordered voices. Therefore, the using measures that can reflect the nonlinear nature of such changes in the acoustical signals is an efficient alternative for the conventional methods. In order to compare and contrast the effectiveness of such approaches, we exploit features such as correlation dimension, the largest Lyapunov exponent, approximate entropy, fractal dimension and ZivLempel complexity, and we also evaluate their performance with respect to some conventional features like jitter and shimmer, in the voice diagnosis task. Using the support vector machine classifier, our simulation results show that correlation dimension and the largest Lyapunov exponent features with the highest recognition rates of 94.44% and 88.89% can be used as a highly reliable method for the clinical diagnosis of vocal folds pathologies and other relevant applications.
nonlinear dynamic analysis voice disorders phase space features