activity
20182022
most citedEnd-to-end spoofing detection with raw waveform CLDNNs

67 citations · 198 across the 20 of their papers we have counts for

collaborators

25 papers

cs.SD2022

LongFNT: Long-form Speech Recognition with Factorized Neural Transducer

Xun Gong, Yu Wu, Jinyu Li +4

Traditional automatic speech recognition~(ASR) systems usually focus on individual utterances, without considering long-form speech with useful historical information, which is mor…

cs.SD20223 cited

Wespeaker: A Research and Production oriented Speaker Embedding Learning Toolkit

Hongji Wang, Chengdong Liang, Shuai Wang +5

Speaker modeling is essential for many related tasks, such as speaker recognition and speaker diarization. The dominant modeling approach is fixed-dimensional vector representation…

cs.SD20224 cited

SJTU-AISPEECH System for VoxCeleb Speaker Recognition Challenge 2022

Zhengyang Chen, Bing Han, Xu Xiang +3

This report describes the SJTU-AISPEECH system for the Voxceleb Speaker Recognition Challenge 2022. For track1, we implemented two kinds of systems, the online system and the offli…

cs.SD202223 cited

Layer-wise Fast Adaptation for End-to-End Multi-Accent Speech Recognition

Xun Gong, Yizhou Lu, Zhikai Zhou +1

Accent variability has posed a huge challenge to automatic speech recognition~(ASR) modeling. Although one-hot accent vector based adaptation systems are commonly used, they requir…

eess.AS2022

End-to-End Multi-speaker ASR with Independent Vector Analysis

Robin Scheibler, Wangyou Zhang, Xuankai Chang +2

We develop an end-to-end system for multi-channel, multi-speaker automatic speech recognition. We propose a frontend for joint source separation and dereverberation based on the in…

cs.SD2022

Summary On The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Grand Challenge

Fan Yu, Shiliang Zhang, Pengcheng Guo +13

The ICASSP 2022 Multi-channel Multi-party Meeting Transcription Grand Challenge (M2MeT) focuses on one of the most valuable and the most challenging scenarios of speech technologie…