The Sheffield language recognition system in NIST LRE 2015

Raymond W. M. Ng, Mauro Nicolao, Oscar Saz, Madina Hasan, Bhusan Chettri, Mortaza Doulaty, Tan Lee, Thomas Hain


The Speech and Hearing Research Group of the University of Sheffield submitted a fusion language recognition system to NIST LRE 2015. It combines three language classifiers. Two are acoustic-based, which use i–vectors and a tandem DNN language recogniser respectively. The third classifier is a phonotactic language recogniser. Two sets of training data with duration of approximately 170 and 300 hours were composed for LR training. Using the larger set of training data, the primary Sheffield LR system gives 32.44 min DCF on the official LR 2015 eval data. A post-evaluation system enhancement was carried out where i–vectors were extracted from the bottleneck features of an English DNN. The min DCF was reduced to 29.20.


DOI: 10.21437/Odyssey.2016-26

Cite as

Ng, R.W.M., Nicolao, M., Saz, O., Hasan, M., Chettri, B., Doulaty, M., Lee, T., Hain, T. (2016) The Sheffield language recognition system in NIST LRE 2015. Proc. Odyssey 2016, 181-187.

Bibtex
@inproceedings{Ng+2016,
author={Raymond W. M. Ng and Mauro Nicolao and Oscar Saz and Madina Hasan and Bhusan Chettri and Mortaza Doulaty and Tan Lee and Thomas Hain},
title={The Sheffield language recognition system in NIST LRE 2015},
year=2016,
booktitle={Odyssey 2016},
doi={10.21437/Odyssey.2016-26},
url={http://dx.doi.org/10.21437/Odyssey.2016-26},
pages={181--187}
}