Descripción
|
|
---|---|
Gender detection from running speech is a very important objective to improve efficiency in tasks as speech or speaker recognition, among others. Traditionally gender detection has been focused on fundamental frequency (f0) and cepstral features derived from voiced segments of speech. The methodology presented here discards f0 as a valid feature because its estimation is complicate, or even impossible in unvoiced fragments, and its relevance in emotional speech or in strongly prosodic speech is not reliable. The approach followed consists in obtaining uncorrelated glottal and vocal tract components which are parameterized as mel-frequency coefficients. K-fold and cross-validation using QDA and GMM classifiers showed detection rates as large as 99.77 in a gender-balanced database of running speech from 340 speakers. | |
Internacional
|
Si |
JCR del ISI
|
No |
Título de la revista
|
Lecture Notes on Artificial Intelligence |
ISSN
|
0302-9743 |
Factor de impacto JCR
|
|
Información de impacto
|
|
Volumen
|
7911 |
DOI
|
|
Número de revista
|
|
Desde la página
|
25 |
Hasta la página
|
32 |
Mes
|
SIN MES |
Ranking
|