no code implementations • 15 Jul 2021 • Qing Chen, Jian Zhang
Most current applications of contrastive learning benefit only a single representation from the last layer of an encoder. In this paper, we propose a multi-level contrasitive learning approach which applies contrastive losses at different layers of an encoder to learn multiple representations from the encoder.
no code implementations • 1 Jan 2021 • Qing Chen, Jian Zhang
Deep neural networks (DNNs) compute representations in a layer by layer fashion, producing a final representation at the top layer of the pipeline, and classification or regression is made using the final representation.
1 code implementation • 2 Jun 2018 • Zhiyuan Tang, Dong Wang, Qing Chen
The third oriental language recognition (OLR) challenge AP18-OLR is introduced in this paper, including the data profile, the tasks and the evaluation principles.
1 code implementation • 28 Jun 2017 • Zhiyuan Tang, Dong Wang, Yixiang Chen, Qing Chen
We present the data profile and the evaluation plan of the second oriental language recognition (OLR) challenge AP17-OLR.
no code implementations • 21 Dec 2016 • Ze Hu, Zhan Zhang, Qing Chen, Haiqin Yang, Decheng Zuo
Finally, a deep belief network (DBN)-based HQA answer quality prediction framework is proposed to predict the quality of answers by learning the high-level hidden semantic representation from the physicians' answers.
no code implementations • 27 Sep 2016 • Dong Wang, Lantian Li, Difei Tang, Qing Chen
We present the AP16-OL7 database which was released as the training and test data for the oriental language recognition (OLR) challenge on APSIPA 2016.
no code implementations • 27 Sep 2016 • Dong Wang, Zhiyuan Tang, Difei Tang, Qing Chen
We present the OC16-CE80 Chinese-English mixlingual speech database which was released as a main resource for training, development and test for the Chinese-English mixlingual speech recognition (MixASR-CHEN) challenge on O-COCOSDA 2016.