Towards Automatic Assessment of Voice Disorders: A Clinical Approach

Purva Barche, Krishna Gurugubelli, Anil Kumar Vuppala

Automatic detection and assessment of voice disorders is important in diagnosis and treatment planning of voice disorders. This work proposes an approach for automatic detection and assessment of voice disorders from a clinical perspective. To accomplish this, a multi-level classification approach was explored in which four binary classifiers were used for the assessment of voice disorders. The binary classifiers were trained using support vector machines with excitation source features, vocal-tract system features, and state-of-art OpenSMILE features. In this study source features namely, glottal parameters obtained from glottal flow waveform, perturbation measures obtained from epoch locations, and cepstral features obtained from linear prediction residual and zero frequency filtered signal were explored. The present study used the Saarbucken voice disorders database to evaluate the performance of proposed approach. The OpenSMILE features namely ComParE and eGEMAPS feature sets shown better performance in terms of classification accuracies of 82.8% and 76%, respectively for voice disorder detection. The combination of excitation source features with baseline feature sets further improved the performance of detection and assessment systems, that highlight the complimentary nature of exciting source features.

 DOI: 10.21437/Interspeech.2020-2160

Cite as: Barche, P., Gurugubelli, K., Vuppala, A.K. (2020) Towards Automatic Assessment of Voice Disorders: A Clinical Approach. Proc. Interspeech 2020, 2537-2541, DOI: 10.21437/Interspeech.2020-2160.

  author={Purva Barche and Krishna Gurugubelli and Anil Kumar Vuppala},
  title={{Towards Automatic Assessment of Voice Disorders: A Clinical Approach}},
  booktitle={Proc. Interspeech 2020},