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Título del libro: Acm International Conference Proceeding Series
Título del capítulo: Design and testing of a corpus for forensic speaker recognition using MFCC, GMM and MLE

Autores UNAM:
JOSE ABEL HERRERA CAMACHO; HECTOR ADRIAN ZUÑIGA SAINOS; GERARDO EUGENIO SIERRA MARTINEZ; JOSE BENITO TRANGOL CURIPE;
Autores externos:

Idioma:

Año de publicación:
2019
Palabras clave:

Audio recordings; Frequency estimation; Gaussian distribution; Maximum likelihood estimation; Object recognition; Speech recognition; Video signal processing; Forensic speaker recognition; Gaussian Mixture Model; Mel frequency cepstral co-efficient; Parametrizations; Recognition models; Spanish language; Speaker recognition system; Speech corpora; Digital forensics


Resumen:

The importance of applying speaker recognition systems in forensic environments has increased in this century. One reason is the use of audio recordings as evidence in trials of every kind. This article presents a voice corpus design with this use in mind, based in recordings from speakers of the Spanish language dialect used in Central Mexico, distinct from any other corpus currently in use. For its evaluation, we used a Mel frequency Cepstral coefficient with a Gaussian Mixture Models for parametrization, and a Maximum Likelihood Estimation approach for classification. Results show an accuracy of more than 93% identification of the speaker at any condition, proving a good recognition model. © 2019 Association for Computing Machinery.


Entidades citadas de la UNAM: