activity
20192022
most citedSmall-footprint Keyword Spotting with Graph Convolutional Network

7 citations · 22 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL20225 cited

A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction

Changzhi Zhou, Dandan Song, Jing Xu +1

Emotion-cause pair extraction (ECPE) is an emerging task in emotion cause analysis, which extracts potential emotion-cause pairs from an emotional document. Most recent studies use…

cs.CL2021

Dynamic Multi-scale Convolution for Dialect Identification

Tianlong Kong, Shouyi Yin, Dawei Zhang +6

Time Delay Neural Networks (TDNN)-based methods are widely used in dialect identification. However, in previous work with TDNN application, subtle variant is being neglected in dif…

cs.AI20206 cited

KVL-BERT: Knowledge Enhanced Visual-and-Linguistic BERT for Visual Commonsense Reasoning

Dandan Song, Siyi Ma, Zhanchen Sun +2

Reasoning is a critical ability towards complete visual understanding. To develop machine with cognition-level visual understanding and reasoning abilities, the visual commonsense…

eess.AS20204 cited

Transformer with Bidirectional Decoder for Speech Recognition

Xi Chen, Songyang Zhang, Dandan Song +2

Attention-based models have made tremendous progress on end-to-end automatic speech recognition(ASR) recently. However, the conventional transformer-based approaches usually genera…

cs.SD2019

THUEE system description for NIST 2019 SRE CTS Challenge

Yi Liu, Tianyu Liang, Can Xu +9

This paper describes the systems submitted by the department of electronic engineering, institute of microelectronics of Tsinghua university and TsingMicro Co. Ltd. (THUEE) to the…

cs.SD20197 cited

Small-footprint Keyword Spotting with Graph Convolutional Network

Xi Chen, Shouyi Yin, Dandan Song +3

Despite the recent successes of deep neural networks, it remains challenging to achieve high precision keyword spotting task (KWS) on resource-constrained devices. In this study, w…