Abstract
The number of people affected by speech problems is increasing as the modern world places increasing demands on the human voice via mobile telephones, voice recognition software, and interpersonal verbal communications. In this paper, we propose a novel methodology for automatic pattern classification of pathological voices. The main contribution of this paper is extraction of meaningful and unique features using Adaptive time-frequency distribution (TFD) and nonnegative matrix factorization (NMF). We construct Adaptive TFD as an effective signal analysis domain to dynamically track the nonstationarity in the speech and utilize NMF as a matrix decomposition (MD) technique to quantify the constructed TFD. The proposed method extracts meaningful and unique features from the joint TFD of the speech, and automatically identifies and measures the abnormality of the signal. Depending on the abnormality measure of each signal, we classify the signal into normal or pathological. The proposed method is applied on the Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database which consists of 161 pathological and 51 normal speakers, and an overall classification accuracy of 98.6% was achieved.
Highlights
Dysphonia or pathological voice refers to speech problems resulting from damage to or malformation of the speech organs
Matching pursuit (MP)-time-frequency distribution (TFD) with Gabor atoms is estimated for each 80 ms of the signal
TF analysis are effective for revealing non-stationary aspects of signals such as trends, discontinuities, and repeated patterns where other signal processing approaches fail or are not as effective; most of the TF analysis are restricted to visualization of TFDs and do not focus on quantification or parametrization that are essential for feature analysis and pattern classification
Summary
Dysphonia or pathological voice refers to speech problems resulting from damage to or malformation of the speech organs. Dysphonia is more common in people who use their voice professionally, for example, teachers, lawyers, salespeople, actors, and singers [1, 2], and it dramatically effects these professional groups’s lives both financially and psychosocially [2]. In the past 20 years, a significant attention has been paid to the science of voice pathology diagnostic and monitoring. The purpose of this work is to help patients with pathological problems for monitoring their progress over the course of voice therapy. Patients are required to routinely visit a specialist to follow up their progress. The traditional ways to diagnose voice pathology are subjective, and depending on the experience of the specialist, different evaluations can be resulted. Developing an automated technique saves time for both the patients and the specialist and can improve the accuracy of the assessments
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