State-of-the-art language recognition systems involve modeling utterances with the i-vectors. However, the uncertainty of the i-vector extraction process represented by the i-vector posterior covariance is affected by various factors such as channel mismatch, background noise, incomplete transformations and duration variability. In this paper, we propose a new quality factor based on the i-vector posterior covariance and incorporate it into the recognition process to improve the recognition accuracy. The experimental results with LRE15 database and various duration conditions show a 2.81% relative improvement in terms of average performance cost as a result of incorporating the proposed quality measure in language recognition systems.
Poorjam, A.H., Saeidi, R., Kinnunen, T., Hautamaki, V. (2016) Incorporating uncertainty as a Quality Measure in I-Vector Based Language Recognition. Proc. Odyssey 2016, 74-80.
@inproceedings{Poorjam+2016,
author={Amir Hossein Poorjam and Rahim Saeidi and Tomi Kinnunen and Ville Hautamaki},
title={Incorporating uncertainty as a Quality Measure in I-Vector Based Language Recognition},
year=2016,
booktitle={Odyssey 2016},
doi={10.21437/Odyssey.2016-11},
url={http://dx.doi.org/10.21437/Odyssey.2016-11},
pages={74--80}
}