Automatic language identification using deep neural networks
Metadatos
Title:
Automatic language identification using deep neural networks
Author:
López-Moreno, Ignacio; González-Domínguez, Javier; Oldrich, Plchot; Martínez, David R.; González-Rodríguez, Joaquín
Entity:
UAM. Departamento de Tecnología Electrónica y de las Comunicaciones
Publisher:
IEEE
Date:
2014-01-01
Citation:
10.1109/ICASSP.2014.6854622
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), IEEE, 2014. 5337-5341
ISSN:
1520-6149 (print)
DOI:
10.1109/ICASSP.2014.6854622
Editor's Version:
http://dx.doi.org/10.1109/ICASSP.2014.6854622
Subjects:
Automatic Language Identification; DNNs; i-vectors; Telecomunicaciones
Note:
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. I. López-Moreno, J. González-Domínguez, P. Oldrich, D. R. Martínez, J. González-Rodríguez, "Automatic language identification using deep neural networks", IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP, Florence (Italy), 2014
Rights:
© 2014 IEEE
Abstract:
This work studies the use of deep neural networks (DNNs)
to address automatic language identification (LID). Motivated
by their recent success in acoustic modelling, we adapt DNNs
to the problem of identifying the language of a given spoken
utterance from short-term acoustic features. The proposed approach
is compared to state-of-the-art i-vector based acoustic
systems on two different datasets: Google 5M LID corpus and
NIST LRE 2009. Results show how LID can largely benefit
from using DNNs, especially when a large amount of training
data is available. We found relative improvements up to 70%,
in Cavg, over the baseline system.
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