no code implementations • 7 Apr 2024 • Jinyi Xu, Zuowei Zhang, Ze Lin, Yixiang Chen, Zhe Liu, Weiping Ding
Moreover, if an object is in the overlapping region of several singleton clusters, it can be assigned to a meta-cluster, defined as the union of these singleton clusters, to characterize the local imprecision in the result.
no code implementations • 5 Nov 2021 • Yuzhi Liang, Yixiang Chen
To alleviate this problem, we propose a new vertical federation learning method, DVFL, which adapts to dynamic data distribution changes through knowledge distillation.
no code implementations • 27 Feb 2018 • Lantian Li, Dong Wang, Yixiang Chen, Ying Shi, Zhiyuan Tang, Thomas Fang Zheng
Various informative factors mixed in speech signals, leading to great difficulty when decoding any of the factors.
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 • 22 Jun 2017 • Miao Zhang, Yixiang Chen, Lantian Li, Dong Wang
This paper proposes a speaker recognition (SRE) task with trivial speech events, such as cough and laugh.
no code implementations • 5 Jun 2017 • Dong Wang, Lantian Li, Ying Shi, Yixiang Chen, Zhiyuan Tang
In this paper, we demonstrated that the speaker factor is also a short-time spectral pattern and can be largely identified with just a few frames using a simple deep neural network (DNN).
no code implementations • 10 May 2017 • Lantian Li, Yixiang Chen, Ying Shi, Zhiyuan Tang, Dong Wang
Recently deep neural networks (DNNs) have been used to learn speaker features.
no code implementations • 9 May 2017 • Zhiyuan Tang, Dong Wang, Yixiang Chen, Lantian Li, Andrew Abel
Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID).
no code implementations • 9 May 2017 • Zhiyuan Tang, Dong Wang, Yixiang Chen, Ying Shi, Lantian Li
Pure acoustic neural models, particularly the LSTM-RNN model, have shown great potential in language identification (LID).
no code implementations • 27 Sep 2016 • Lantian Li, Yixiang Chen, Dong Wang, Chenghui Zhao
PLDA is a popular normalization approach for the i-vector model, and it has delivered state-of-the-art performance in speaker verification.