activity
20152023
most citedClassify or Select: Neural Architectures for Extractive Document Summarization

83 citations · 271 across the 30 of their papers we have counts for

collaborators

30 papers

cs.SD2023★ 12 cited

Efficient Neural Music Generation

Max W. Y. Lam, Qiao Tian, Tang Li +10

Recent progress in music generation has been remarkably advanced by the state-of-the-art MusicLM, which comprises a hierarchy of three LMs, respectively, for semantic, coarse acous…

eess.AS2022

Zero-Shot Accent Conversion using Pseudo Siamese Disentanglement Network

Dongya Jia, Qiao Tian, Kainan Peng +5

The goal of accent conversion (AC) is to convert the accent of speech into the target accent while preserving the content and speaker identity. AC enables a variety of applications…

cs.CL2022

Data-Driven Adaptive Simultaneous Machine Translation

Guangxu Xun, Mingbo Ma, Yuchen Bian +7

In simultaneous translation (SimulMT), the most widely used strategy is the wait-k policy thanks to its simplicity and effectiveness in balancing translation quality and latency. H…

eess.AS2022★ 1 cited

AT: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing

He Bai, Renjie Zheng, Junkun Chen +3

Recently, speech representation learning has improved many speech-related tasks such as speech recognition, speech classification, and speech-to-text translation. However, all the…

cs.CL2021

Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR

Junkun Chen, Mingbo Ma, Renjie Zheng +1

Simultaneous speech-to-text translation is widely useful in many scenarios. The conventional cascaded approach uses a pipeline of streaming ASR followed by simultaneous MT, but suf…

cs.CL2021★ 6 cited

Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation

Renjie Zheng, Junkun Chen, Mingbo Ma +1

Recently, representation learning for text and speech has successfully improved many language related tasks. However, all existing methods suffer from two limitations: (a) they onl…