title: Classification of vocal aging using parameters extracted from the glottal signal.
abstract: This article proposes and evaluates a method to classify vocal aging using artificial neural network (ANN) and support vector machine (SVM), using the parameters extracted from the speech signal as inputs. For each recorded speech, from a corpus of male and female speakers of different ages, the corresponding glottal signal is obtained using an inverse filtering algorithm. The Mel Frequency Cepstrum Coefficients (MFCC) also extracted from the voice signal and the features extracted from the glottal signal are supplied to an ANN and an SVM with a previous selection. The selection is performed by a wrapper approach of the most relevant parameters. Three groups are considered for the aging-voice classification: young (aged 15-30 years), adult (aged 31-60 years), and senior (aged 61-90 years). The results are compared using different possibilities: with only the parameters extracted from the glottal signal, with only the MFCC, and with a combination of both. The results demonstrate that the best classification rate is obtained using the glottal signal features, which is a novel result and the main contribution of this article.
authors.affiliation: Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Rio de Janeiro, Brazil. Electronic address: leofome@hotmail.com.
authors.forename: Leonardo A
authors.initials: LA
authors.lastname: Forero Mendoza
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
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: Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Rio de Janeiro, Brazil.
authors.forename: Marco A
authors.initials: MA
authors.lastname: Silva
authors.affiliation: Electrical Engineering Department, Instituto Militar de Engenharia (IME), Rio de Janeiro, Rio de Janeiro, Brazil.
authors.forename: José A
authors.initials: JA
authors.lastname: Apolinário
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology*; D000465:Algorithms; D005260:Female; D005931:Glottis / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D010699:Phonation / Q000502:physiology*; D013018:Sound Spectrography; D013068:Speech Production Measurement / Q000379:methods; D014827:Vocal Cords / Q000502:physiology*; D014831:Voice / Q000502:physiology*; D014833:Voice Quality
keywords: Glottal source; Neural network; Speech processing; Vocal aging