1 code implementation • 17 Sep 2023 • Qiuming Zhao, Guangzhi Sun, Chao Zhang, Mingxing Xu, Thomas Fang Zheng
Recent end-to-end automatic speech recognition (ASR) models have become increasingly larger, making them particularly challenging to be deployed on resource-constrained devices.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 31 May 2022 • Mingxing Xu, Chenglin Li, Wenrui Dai, Siheng Chen, Junni Zou, Pascal Frossard, Hongkai Xiong
Specifically, adaptive spherical wavelets are learned with a lifting structure that consists of trainable lifting operators (i. e., update and predict operators).
no code implementations • 27 Apr 2022 • Mingxing Xu, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong
Subsequently, this local information is aligned and propagated to the preserved nodes to alleviate information loss in graph coarsening.
no code implementations • EMNLP 2021 • YingMei Guo, Linjun Shou, Jian Pei, Ming Gong, Mingxing Xu, Zhiyong Wu, Daxin Jiang
Although various data augmentation approaches have been proposed to synthesize training data in low-resource target languages, the augmented data sets are often noisy, and thus impede the performance of SLU models.
no code implementations • 3 Aug 2021 • Mingxing Xu, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, Pascal Frossard
To ensure that the learned graph representations are invariant to node permutations, a layer is employed at the input of the networks to reorder the nodes according to their local topology information.
no code implementations • SEMEVAL 2020 • YingMei Guo, Jinfa Huang, Yanlong Dong, Mingxing Xu
In our system, we utilize five types of representation of data as input of base classifiers to extract information from different aspects.
no code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • YingMei Guo, Zhiyong Wu, Mingxing Xu
Unlike non-conversation scenes, emotion recognition in dialogues (ERD) poses more complicated challenges due to its interactive nature and intricate contextual information.
1 code implementation • 9 Jan 2020 • Mingxing Xu, Wenrui Dai, Chunmiao Liu, Xing Gao, Weiyao Lin, Guo-Jun Qi, Hongkai Xiong
In this paper, we propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) that leverages dynamical directed spatial dependencies and long-range temporal dependencies to improve the accuracy of long-term traffic forecasting.
no code implementations • SEMEVAL 2019 • Xihao Liang, Ye Ma, Mingxing Xu
In this paper, we describe our hierarchical ensemble system designed for the SemEval-2019 task3, EmoContext.
no code implementations • 17 Nov 2016 • Xi Ma, Zhiyong Wu, Jia Jia, Mingxing Xu, Helen Meng, Lianhong Cai
Hence, traditional methods may fail to distinguish some of the emotions with just one global feature subspace.