activity
20202022
most citedINTERSPEECH 2021 ConferencingSpeech Challenge: Towards Far-field Multi-Channel Speech Enhancement for Video Conferencing

7 citations · 11 across the 4 of their papers we have counts for

collaborators

6 papers

cs.SD20221 cited

The ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge (ICSRC): Dataset, Tracks, Baseline and Results

Ao Zhang, Fan Yu, Kaixun Huang +7

This paper summarizes the outcomes from the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge (ICSRC). We first address the necessity of the challenge and then introduce…

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…

eess.AS20213 cited

The Multi-speaker Multi-style Voice Cloning Challenge 2021

Qicong Xie, Xiaohai Tian, Guanghou Liu +9

The Multi-speaker Multi-style Voice Cloning Challenge (M2VoC) aims to provide a common sizable dataset as well as a fair testbed for the benchmarking of the popular voice cloning t…

eess.AS20217 cited

INTERSPEECH 2021 ConferencingSpeech Challenge: Towards Far-field Multi-Channel Speech Enhancement for Video Conferencing

Wei Rao, Yihui Fu, Yanxin Hu +11

The ConferencingSpeech 2021 challenge is proposed to stimulate research on far-field multi-channel speech enhancement for video conferencing. The challenge consists of two separate…

cs.SD2021

AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario

Yihui Fu, Luyao Cheng, Shubo Lv +10

In this paper, we present AISHELL-4, a sizable real-recorded Mandarin speech dataset collected by 8-channel circular microphone array for speech processing in conference scenario.…

cs.SD2020

AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines

Yao Shi, Hui Bu, Xin Xu +2

In this paper, we present AISHELL-3, a large-scale and high-fidelity multi-speaker Mandarin speech corpus which could be used to train multi-speaker Text-to-Speech (TTS) systems. T…