Classification of functional dysphonia using the tunable Q wavelet transform

Functional dysphonia (FD) refers to an abnormality in voice quality in the absence of an identifiable lesion. In this paper, we propose an approach based on the tunable Q wavelet transform (TQWT) to automatically classify two types of FD (hyperfunctional dysphonia and hypofunctional dysphonia) from a healthy voice using the acoustic voice signal. Using TQWT, voice signals were decomposed into sub-bands and the entropy values extracted from the sub-bands were utilized as features for the studied 3-class classification problem. In addition, the Mel-frequency cepstral coefficient (MFCC) and glottal features were extracted from the acoustic voice signal and the estimated glottal source signal, respectively. A convolutional neural network (CNN) classifier was trained separately for the TQWT, MFCC and glottal features. Experiments were conducted using voice signals of 57 healthy speakers and 113 FD patients (72 with hyperfunctional dysphonia and 41 with hypofunctional dysphonia) taken from the VOICED database. These experiments revealed that the TQWT features yielded an absolute improvement of 5.5% and 4.5% compared to the baseline MFCC features and glottal features, respectively. Furthermore, the highest classification accuracy (67.91%) was obtained
using the combination of the TQWT and glottal features, which indicates the complementary nature of these features.