Automatic Language Identification Using Deep Neural Networks
Venue
Proc. ICASSP, IEEE (2014)
Publication Year
2014
Authors
Ignacio Lopez-Moreno, Javier Gonzalez-Dominguez, Oldrich Plchot
BibTeX
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.
