Loading...

Significant pathological voice discrimination by computing posterior distribution of balanced accuracy

Pakravan, M ; Sharif University of Technology | 2022

198 Viewed
  1. Type of Document: Article
  2. DOI: 10.1016/j.bspc.2021.103410
  3. Publisher: Elsevier Ltd , 2022
  4. Abstract:
  5. The ability to speak lucidly plays a key role in social relations. Consequently, the role of the larynx is quite important, and timely diagnosis of laryngeal diseases has proved to be crucial. In this study, a simple computational model for inverse of speech production model is employed to extract the glottal waveform using speech signal. This waveform has useful information about vocal folds performance in terms of providing evidence for distinguishing pathological disorders. Furthermore, obtaining the significance of classification results is important, because it leads to reliable inferences. This study utilizes the sustained vowel sound /a/ and a well-referenced database, namely MEEI. In this work, after extraction of six discriminating features by using appropriate signal modeling and processing methods and upon change of the feature space by using Kernel Principal Component Analysis (KPCA), a classifier consisting of Naïve Bayes and Fisher Linear Discriminant, is exerted on the feature sets. Regarding voice pathology detection, the proposed approach achieved a significant classification balanced accuracy 93.6%±0.03 with p-value <0.01 for normal and abnormal classification using the Beta distribution model for the posterior distribution of the average of the cross-validation results. The proposed features are also compared with some conventional features in this field. The results show significant improved performance for the proposed features in discriminating different types of pathological voices. © 2021 Elsevier Ltd
  6. Keywords:
  7. Fisher linear discriminant ; Glottal waveform ; Kernel principal component analysis ; Naïve Bayes classifier ; Classification (of information) ; Diagnosis ; Speech recognition ; Fisher's linear discriminant ; Glottal waveform ; Kernel principal component analyses (KPCA) ; Naive Bayes classifiers ; Pathological voice ; Performance ; Posterior distribution of balanced accuracy ; Posterior distributions ; Social relations ; Waveforms ; Inverse problems ; Autocorrelation ; Bayesian learning ; Classifier ; Computer model ; Controlled study ; Cross validation ; Diagnostic accuracy ; Diagnostic test accuracy study ; Discrete Fourier transform ; Edema ; Feature extraction ; Frequency modulation ; Human ; Human experiment ; Larynx ; Larynx disorder ; Oscillation ; Paralysis ; Paresis ; Polyp ; Principal component analysis ; Social interaction ; Speech ; Speech disorder ; Vocal cord ; Voice recognition ; Waveform
  8. Source: Biomedical Signal Processing and Control ; Volume 73 , 2022 ; 17468094 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S1746809421010077