This paper describes data collection efforts conducted as part of the RedDots project which is dedicated to the study of speaker recognition under conditions where test utterances are of short duration and of variable phonetic content. At the current stage, we focus on English speakers, both native and non-native, recruited worldwide. This is made possible through the use of a recording front-end consisting of an application running on mobile devices communicating with a centralized web server at the back-end. Speech recordings are collected by having speakers read text prompts displayed on the screen of the mobile devices. We aim to collect a large number of sessions from each speaker over a long time span, typically one session per week over a one year period. The corpus is expected to include rich inter-speaker and intra-speaker variations, both intrinsic and extrinsic (that is, due to recording channel and acoustic environment).
Cite as: Lee, K.A., Larcher, A., Wang, G., Kenny, P., Brümmer, N., Leeuwen, D.v., Aronowitz, H., Kockmann, M., Vaquero, C., Ma, B., Li, H., Stafylakis, T., Alam, M.J., Swart, A., Perez, J. (2015) The reddots data collection for speaker recognition. Proc. Interspeech 2015, 2996-3000, doi: 10.21437/Interspeech.2015-95
@inproceedings{lee15_interspeech,
author={Kong Aik Lee and Anthony Larcher and Guangsen Wang and Patrick Kenny and Niko Brümmer and David van Leeuwen and Hagai Aronowitz and Marcel Kockmann and Carlos Vaquero and Bin Ma and Haizhou Li and Themos Stafylakis and Md. Jahangir Alam and Albert Swart and Javier Perez},
title={{The reddots data collection for speaker recognition}},
year=2015,
booktitle={Proc. Interspeech 2015},
pages={2996--3000},
doi={10.21437/Interspeech.2015-95}
}