In this paper, we present our XMUSPEECH system for the oriental language recognition (OLR) challenge, AP19-OLR. The challenge this year contained three tasks: (1) short-utterance LID, (2) cross-channel LID, and (3) zero-resource LID. We leveraged the system pipeline from three aspects, including front-end training, back-end processing, and fusion strategy. We implemented many encoder networks for Tasks 1 and 3, such as extended x-vector, multi-task learning x-vector with phonetic information, and our previously presented multi-feature integration structure. Furthermore, our previously proposed length expansion method was used in the test set for Task 1. I-vector systems based on different acoustic features were built for the cross-channel task. For all of three tasks, the same back-end procedure was used for the sake of stability but with different settings for three tasks. Finally, the greedy fusion strategy helped to choose the subsystems to compose the final fusion systems (submitted systems). Cavg values of 0.0263, 0.2813, and 0.1697 from the development set for Task 1, 2, and 3 were obtained from our submitted systems, and we achieved rank 3rd, 3rd, and 1st in the three tasks in this challenge, respectively.
Cite as: Li, Z., Zhao, M., Li, J., Zhi, Y., Li, L., Hong, Q. (2020) The XMUSPEECH System for the AP19-OLR Challenge. Proc. Interspeech 2020, 452-456, doi: 10.21437/Interspeech.2020-1923
@inproceedings{li20e_interspeech,
author={Zheng Li and Miao Zhao and Jing Li and Yiming Zhi and Lin Li and Qingyang Hong},
title={{The XMUSPEECH System for the AP19-OLR Challenge}},
year=2020,
booktitle={Proc. Interspeech 2020},
pages={452--456},
doi={10.21437/Interspeech.2020-1923}
}