PPRLM language recognition systems using context-dependent (CD) phone recognizers outperform significantly those using contextindependent (CI) phone recognizers, but computationally are less efficient. This papers describes a very efficient PPRLM system both in terms of performances and processing speed. The system uses CI phone recognizers trained with MLP features concatenated with the conventional PLP and pitch features. MLP features have some interesting properties that make them suitable to build such a system, in particular the temporal context provided to the inputs of the MLP and the discriminative criterion used to learn MLP parameters. Results of preliminary experiments conducted on the NIST LRE 2005 for closed-set task show significant improvements (for the three conditions) obtained by the proposed system compared to a PPRLM system using CI phone models trained with PLP features. More ever, the proposed system performs equally compared to the PPRLM using CD phone models, while running 6 times faster.
Index Terms: Language recognition, Phonotactic approach, MLP features
Cite as: BenZeghiba, M.F., Gauvain, J.-L., Lamel, L. (2012) Phonotactic language recognition using MLP features. Proc. Interspeech 2012, 2041-2044, doi: 10.21437/Interspeech.2012-545
@inproceedings{benzeghiba12_interspeech,
author={Mohamed Faouzi BenZeghiba and Jean-Luc Gauvain and Lori Lamel},
title={{Phonotactic language recognition using MLP features}},
year=2012,
booktitle={Proc. Interspeech 2012},
pages={2041--2044},
doi={10.21437/Interspeech.2012-545}
}