title: Analysis and Classification of Voice Pathologies Using Glottal Signal Parameters.
abstract: The classification of voice diseases has many applications in health, in diseases treatment, and in the design of new medical equipment for helping doctors in diagnosing pathologies related to the voice. This work uses the parameters of the glottal signal to help the identification of two types of voice disorders related to the pathologies of the vocal folds: nodule and unilateral paralysis. The parameters of the glottal signal are obtained through a known inverse filtering method, and they are used as inputs to an Artificial Neural Network, a Support Vector Machine, and also to a Hidden Markov Model, to obtain the classification, and to compare the results, of the voice signals into three different groups: speakers with nodule in the vocal folds; speakers with unilateral paralysis of the vocal folds; and speakers with normal voices, that is, without nodule or unilateral paralysis present in the vocal folds. The database is composed of 248 voice recordings (signals of vowels production) containing samples corresponding to the three groups mentioned. In this study, a larger database was used for the classification when compared with similar studies, and its classification rate is superior to other studies, reaching 97.2%.
authors.affiliation: Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Rio de Janeiro, Brazil. Electronic address: mendonza@ele.puc-rio.br.
authors.forename: Leonardo A
authors.initials: LA
authors.lastname: Forero M
authors.affiliation: Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Rio de Janeiro, Brazil.
authors.forename: Manoela
authors.initials: M
authors.lastname: Kohler
authors.affiliation: Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Rio de Janeiro, Brazil.
authors.forename: Marley M B R
authors.initials: MM
authors.lastname: Vellasco
authors.affiliation: Applied Mathematics Department, Graduation Program in Telecommunications Engineering, Universidade Federal Fluminense (UFF), Niterói, Rio de Janeiro, Brazil.
authors.forename: Edson
authors.initials: E
authors.lastname: Cataldo
mesh_terms: D000162:Acoustics; D016022:Case-Control Studies; D016208:Databases, Factual; D005931:Glottis / Q000503:physiopathology*; D006801:Humans; D008390:Markov Chains; D016571:Neural Networks, Computer; D010699:Phonation; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013061:Speech Acoustics; D013068:Speech Production Measurement / Q000379:methods*; D060388:Support Vector Machine; D013997:Time Factors; D014826:Vocal Cord Paralysis / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology; D014827:Vocal Cords / Q000503:physiopathology; D014832:Voice Disorders / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology; D014833:Voice Quality
keywords: Classification of vocal fold pathologies; Glottal signal parameters; Neural network; Support vector machine