Comparative Study of Filter Banks to Improve the Performance of Voice Disorder Assessment Systems using LTAS Features

Objective assessment of voice disorders is widely explored as an early diagnosis tool for the classification of voice disorders. Voice disorders affect the pitch, loudness and voice quality, which are perceived at the suprasegmental-level in the speech signal. For the detection and assessment of voice disorders, this study explores the effectiveness of Long Term Average Spectral (LTAS) features using four state-of-the-art filter banks designed with critical-band, constant-Q, gammatone, and single-frequency filtering approaches. Moreover, the performance of the systems is compared with state-of-the-art statistical-average and openSMILE features. Voice disorder detection experiment was carried out on SVD and HUPA database, while only SVD database is used for assessment task. Assessment task is performed in clinical way, in which four binary classifiers were trained in our study. Voice disorder detection and assessment tasks were carried out using the support vector machine classifier. From the results, it was observed that constant-Q filter bank based LTAS features performed better among all LTAS features with classification accuracy of 78% and 81.4% for voice disorder detection task on SVD and HUPA database, respectively. Further, the combination of LTAS features with OpenSMILE features improved (89.6% and 86.6% for SVD and HUPA database, respectively) the performance.