Classification of vocal aging using parameters extracted from the glottal signal.
J Voice
; 28(5): 532-7, 2014 Sep.
Article
em En
| MEDLINE
| ID: mdl-24880675
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Fonação
/
Prega Vocal
/
Voz
/
Qualidade da Voz
/
Algoritmos
/
Envelhecimento
/
Glote
Limite:
Adult
/
Aged
/
Aged80
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
J Voice
Assunto da revista:
OTORRINOLARINGOLOGIA
Ano de publicação:
2014
Tipo de documento:
Article