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- Voice Pathologies Identification Speech signals, features and classifiers evaluationPublication . Cordeiro, Hugo; Fonseca, José; Guimarães, Isabel; Meneses, CarlosVoice pathology identification using speech processing methods can be used as a preliminary diagnosis. This study implements a set of identification systems to screen voice pathologies using voice signal features from the sustained vowel /a/ and continuous speech. The two signals tasks are evaluated using three acoustic features applied to four classifiers. Three main classes are identified: physiological disorders; neuromuscular disorders; and healthy subjects. The main objective of this work is to evaluate which voice signal is more reliable for voice pathology diagnosis, which acoustic feature has more pathology information and which is the best classifier to carry out this task. The best overall system accuracy is 77.9%, obtained with Mel-Line Spectrum Frequencies (MLSF) feature extracted from continuous speech and applied to a Gaussian Mixture Models (GMM) classifier.
- Hierarchical classification and system combination for automatically identifying physiological and neuromuscular laryngeal pathologiesPublication . Cordeiro, Hugo; Fonseca, Jose; Guimarães, Isabel; Meneses, CarlosObjectives. Speech signal processing techniques have provided several contributions to pathologic voice identification, in which healthy and unhealthy voice samples are evaluated. A less common approach is to identify laryngeal pathologies, for which the use of a noninvasive method for pathologic voice identification is an important step forward for preliminary diagnosis. In this study, a hierarchical classifier and a combination of systems are used to improve the accuracy of a three-class identification system (healthy, physiological larynx pathologies, and neuromuscular larynx pathologies). Method. Three main subject classes were considered: subjects with physiological larynx pathologies (vocal fold nodules and edemas: 59 samples), subjects with neuromuscular larynx pathologies (unilateral vocal fold paralysis: 59 samples), and healthy subjects (36 samples). The variables used in this study were a speech task (sustained vowel /a/ or continuous reading speech), features with or without perceptual information, and features with or without direct information about formants evaluated using single classifiers. A hierarchical classification system was designed based on this information. Results. The resulting system combines an analysis of continuous speech by way of the commonly used sustained vowel /a/ to obtain spectral and perceptual speech features. It achieved an accuracy of 84.4%, which represents an improvement of approximately 9% compared with the stand-alone approach. For pathologic voice identification, the accuracy obtained was 98.7%, and the identification accuracy for the two pathology classes was 81.3%. Conclusions. Hierarchical classification and system combination create significant benefits and introduce a modular approach to the classification of larynx pathologies.