title: Evolving Spiking Neural Networks for Recognition of Aged Voices.
abstract: The aging of the voice, known as presbyphonia, is a natural process that can cause great change in vocal quality of the individual. This is a relevant problem to those people who use their voices professionally, and its early identification can help determine a suitable treatment to avoid its progress or even to eliminate the problem. This work focuses on the development of a new model for the identification of aging voices (independently of their chronological age), using as input attributes parameters extracted from the voice and glottal signals. The proposed model, named Quantum binary-real evolving Spiking Neural Network (QbrSNN), is based on spiking neural networks (SNNs), with an unsupervised training algorithm, and a Quantum-Inspired Evolutionary Algorithm that automatically determines the most relevant attributes and the optimal parameters that configure the SNN. The QbrSNN model was evaluated in a database composed of 120 records, containing samples from three groups of speakers. The results obtained indicate that the proposed model provides better accuracy than other approaches, with fewer input attributes.
authors.affiliation: Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rua Marquês de São Vicente, 225, 22.451-900, Rio de Janeiro, RJ, Brazil. Electronic address: mabs21@ele.puc-rio.br.
authors.forename: Marco
authors.initials: M
authors.lastname: Silva
authors.affiliation: Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rua Marquês de São Vicente, 225, 22.451-900, Rio de Janeiro, RJ, Brazil.
authors.forename: Marley M B R
authors.initials: MMBR
authors.lastname: Vellasco
authors.affiliation: Applied Mathematics Department, Graduation Program in Telecommunications Engineering, Universidade Federal Fluminense (UFF), Rua Mário Santos Braga, S/N, Centro, 24.120-040, Niterói, RJ, Brazil.
authors.forename: Edson
authors.initials: E
authors.lastname: Cataldo
mesh_terms: D000162:Acoustics; D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D046709:Pattern Recognition, Physiological; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013061:Speech Acoustics; D013068:Speech Production Measurement / Q000379:methods*; D014833:Voice Quality; D055815:Young Adult
keywords: Aging voice; Clustering; Evolutionary algorithms; Glottal signal; Spiking neural network