A methodology for voice classification based on the personalized fundamental frequency estimation

Nowadays, the incidence of voice disorders is increasing rapidly, with about a third of the population suffering from dysphonia at some point in their lives. Dysphonia is a disorder that alters vocal quality and can impair and reduce the quality of life. The structural or functional alteration of the phonatory apparatus, unhealthy lifestyles or an excessive use of the vocal cords for work activities (e.g. teaching) can cause voice disorders. Unfortunately, people who suffer from dysphonia often underestimate its symptoms and therefore delay consulting a speech therapist for accurate voice assessment and treatment. Voice disorder evaluation involves a series of tests, including an acoustic analysis. This quantifies the measurements of voice quality through the evaluation of certain characteristic parameters, for example the fundamental frequency (F-0). In this paper, a personalized methodology for the estimation of the F-0 is presented. The personalization is accomplished by taking into account two of the main factors that influence the F-0, the gender and age of the subject. The estimation of the F-0 is crucial for the classification of the voice signal, because the discrimination of a healthy voice from a pathological one is achieved by evaluating the inclusion of the F-0 value within the healthy range. To evaluate the presented methodology, we have carried out a set of tests by using some voice signals selected from an available database in order to compare the classification ability of the proposed methodology with other algorithms existing in the literature. The numerical results obtained show that the proposed methodology provides a good accuracy, sensitivity, and specificity, respectively of over 77%, 72% and 81%, values better than those achieved by the most frequently other used and cited fundamental frequency estimation algorithms. Additionally, a statistical analysis to evaluate whether or not a statistically significant difference exists between the accuracy, sensitivity and specificity has been carried out. The outcome of the ANOVA tests and of the t-tests confirms that there is a significant difference between the proposed methodology and the other algorithms. Finally, the presented methodology could be embedded in a portable and simple m-health application that could be useful for the monitoring of the state of vocal health and the prevention of voice disorders. (C) 2018 Elsevier Ltd. All rights reserved.