Automatic language identification using deep neural networks

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 a...

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Bibliographic Details
Main Authors: Lopez-Moreno, Ignacio, Gonzalez-Dominguez, Javier, Plchot, Oldrich, Martinez, David, Gonzalez-Rodriguez, Joaquin, Moreno, Pedro
Format: Conference Proceeding
Language:English
Subjects:
Acoustics
Automatic Language Identification
DNNs
Google
i-vectors
Neural networks
NIST
Speech
Speech recognition
Training
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Summary: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 C avg , over the baseline system.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2014.6854622