Abstract
The identification and classification of pathological voice are still a challenging area of research in speech processing. Acoustic features of speech are used mainly to discriminate normal voices from pathological voices. This paper explores and compares various classification models to find the ability of acoustic parameters in differentiating normal voices from pathological voices. An attempt is made to analyze and to discriminate pathological voice from normal voice in children using different classification methods. The classification of pathological voice from normal voice is implemented using Support Vector Machine (SVM) and Radial Basis Functional Neural Network (RBFNN). The normal and pathological voices of children are used to train and test the classifiers. A dataset is constructed by recording speech utterances of a set of Tamil phrases. The speech signal is then analyzed in order to extract the acoustic parameters such as the Signal Energy, pitch, formant frequencies, Mean Square Residual signal, Reflection coefficients, Jitter and Shimmer. In this study various acoustic features are combined to form a feature set, so as to detect voice disorders in children based on which further treatments can be prescribed by a pathologist. Hence, a successful pathological voice classification will enable an automatic non-invasive device to diagnose and analyze the voice of the patient.
Highlights
In the past 20 years, a significant attention has been paid to the science of voice pathology diagnostic and monitoring
The feature vector is constructed using the peaks of Formant frequencies, average pitch period, the signal energy, mean square residual signal, reflection coefficients, jitter and shimmer
In this paper several acoustic techniques for extracting different acoustic parameters and providing a hybrid approach of feature extraction are presented. The purpose of this methodology is to classify the voice dataset into normal and pathological voice and to compare the classification performance based on classification accuracy using Support Vector Machine and Radial Basis Functional Neural Network
Summary
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. The traditional ways to diagnose voice pathology are subjective, invasive methods such as the direct inspection of the vocal folds and the observations of the vocal folds by endoscopic instruments are done. These techniques are expensive, risky, time consuming, discomfort to the patients and require costly resources, such as special light sources, endoscopic instruments and specialized video-camera equipment. In order to circumvent the above problems, non-invasive methods have been developed to help the ENT clinicians and speech therapists for early detection of vocal fold pathology and can improve the accuracy of the assessments. The voice disorders are caused due to defects in the speech organs, mental illness, hearing impairment, autism, paralysis or multiple disabilities
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