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Top1. Introduction
The risk of pathological voice related disorders has increased manifolds. This is due to modern lifestyle, environmental issues, self medications and even a profession. About 25% of the population is engaged in activities that are “vocally demanding” (Amami & Smiti, 2017). The examples include professors, lawyers, auctioneers, aerobics instructors, singers, actors and manufacturing supervisors. For the diagnosis of voice pathologies, invasive endoscopy procedures are the current state of the art. But recently non-invasive digital techniques (like voice profiling and image processing) have evolved and are assisting medical professionals for early detection of voice disorders. In voice based detection, the most common method for extracting voice features is determination of acoustic parameters directly from the voice signal. Since most of the voice disorders are due to vocal fold dynamics, the researchers have started to work with glottal parameters of vocal folds to expedite the detection of related disorders. The detection of voice pathologies needs further improvement so as to increase the accuracy of voice detection as well as their classification. This work aims improved detection and classification of voice disorders using vocal folds glottal, physical, acoustic, and equivalent electrical parameters.
Vocal acoustic evaluation is popularly used for the assessment and diagnosis of voice disorders (Teixeira et al., 2020). Xiao Yao et al. claimed that when the speaker is under stress, certain vocal organs are affected (Yao et al., 2015).Xiao Yao et al. further discussed the physical parameter, glottal flow and stress output relationships(Yao et al., 2018). Although the voice parameters like the vibration of the vocal folds, shape of the glottis and the glottal airflow have been extensively researched in literature, yet their individual impacts on the voice quality cannot be accurately computed (Ramsay, 2019). It is a fact that thorough study and evaluation of vocal folds behavior essentially require characterization of vocal folds and their relationship with vocal tract. This paper focuses on the diagnosis of pathological voice using physical, glottal parameters as well as acoustic and its equivalent electrical parameters.
The major contributions of this paper are summarized as under:
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It is a fact that speech disorders in the voice is caused fundamentally by the physiological changes of vocal folds leading to deviation in their natural vibrations and are reflected by glottal flow. The authors have proposed a novel method to find physical parameters of vocal folds. On the other hand IAIF method is used to extract glottal flow parameters from the given voice samples. The physical parameters are then correlated with glottal flow parameters. Any change in glottal parameters reflects change in physical parameters which are then utilized to classify pathological voices. Hence the contents of the paper presents a method for detection and classification of voice disorders based on physical speech production model and characteristics of glottal flow.
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Furthermore, authors have also developed relation between vocal folds length and parameters of the acoustic model of the vocal folds. Change in vocal folds length, due to voice disorders reflects change in acoustic model parameters and the same has been used to classify pathological voices. The results of acoustic parameters variations due to voice disorders are shown in Table 10 at page-15 of the manuscript.
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It has also been experimented that current variation in equivalent electrical model is a function of change in vocal folds length. This feature has been used to classify pathological voices as shown in Figure 16.