This paper presents a technique to adapt an acoustically based language classifier to the background conditions and speaker accents. This adaptation improves language classification on a broad spectrum of TV broadcasts. The core of the system consists of an iVector-based setup in which language and channel variabilities are modeled separately. The subsequent language classifier (the backend) operates on the language factors, i.e. those features in the extracted iVectors that explain the observed language variability. The proposed technique adapts the language variability model to the background conditions and to the speaker accents present in the audio. The effect of the adaptation is evaluated on a 28 hours corpus composed of documentaries and monolingual as well as multilingual broadcast news shows. Consistent improvements in the automatic identification of Flemish (Belgian Dutch), English and French are demonstrated for all broadcast types.
Cite as: Desplanques, B., Demuynck, K., Martens, J.-P. (2014) Robust language recognition via adaptive language factor extraction. Proc. Interspeech 2014, 2160-2164, doi: 10.21437/Interspeech.2014-484
@inproceedings{desplanques14_interspeech,
author={Brecht Desplanques and Kris Demuynck and Jean-Pierre Martens},
title={{Robust language recognition via adaptive language factor extraction}},
year=2014,
booktitle={Proc. Interspeech 2014},
pages={2160--2164},
doi={10.21437/Interspeech.2014-484}
}