Classification of muscle tension dysphonia (MTD) female speech and normal speech using cepstrum variables and random forest algorithm*

Abstract

This study investigated the acoustic characteristics of sustained vowel /a/ and sentence utterance produced by patients with muscle tension dysphonia (MTD) using cepstrum-based acoustic variables. 36 women diagnosed with MTD and the same number of women with normal voice participated in the study and the data were recorded and measured by ADSV™. The results demonstrated that cepstral peak prominence (CPP) and CPP_F0 among all of the variables were statistically significantly lower than those of control group. When it comes to the GRBAS scale, overall severity (G) was most prominent, and roughness (R), breathiness (B), and strain (S) indices followed in order in the voice quality of MTD patients. As these characteristics increased, a statistically significant negative correlation was observed in CPP. We tried to classify MTD and control group using CPP and CPP_F0 variables. As a result of statistic modeling with a Random Forest machine learning algorithm, much higher classification accuracy (100% in training data and 83.3% in test data) was found in the sentence reading task, with CPP being proved to be playing a more crucial role in both vowel and sentence reading tasks.

Investigation of the Clinical Effectiveness and Prognostic Factors of Voice Therapy in Voice Disorders: A Pilot Study

“…Numerous artificial neural network models have been created for diverse applications across various fields [28][29][30][31]. Within this array of network models, the multilayer feedforward artificial neural network (MLP) stands out as being one of the most frequently employed, and it was also the model of choice in our study [32][33][34][35].…”