Open Speech and Language Resources


Identifier: SLR111

Summary: A Free Mandarin Multi-channel Meeting Speech Corpus, provided by Beijing Shell Shell Technology Co.,Ltd

Category: Speech

License: CC BY-SA 4.0

Downloads (use a mirror closer to you):
train_L.tar.gz [7.0G]   ( Training set of large room, 8-channel microphone array speech )   Mirrors: [US]   [EU]   [CN]  
train_M.tar.gz [25G]   ( Training set of medium room, 8-channel microphone array speech )   Mirrors: [US]   [EU]   [CN]  
train_S.tar.gz [14G]   ( Training set of small room, 8-channel microphone array speech )   Mirrors: [US]   [EU]   [CN]  
test.tar.gz [5.2G]   ( Test set )   Mirrors: [US]   [EU]   [CN]  

About this resource:

The AISHELL-4 is a sizable real-recorded Mandarin speech dataset collected by 8-channel circular microphone array for speech processing in conference scenarios. The dataset consists of 211 recorded meeting sessions, each containing 4 to 8 speakers, with a total length of 120 hours. This dataset aims to bridge the advanced research on multi-speaker processing and the practical application scenario in three aspects. With real recorded meetings, AISHELL-4 provides realistic acoustics and rich natural speech characteristics in conversation such as short pause, speech overlap, quick speaker turn, noise, etc. Meanwhile, the accurate transcription and speaker voice activity are provided for each meeting in AISHELL-4. This allows the researchers to explore different aspects in meeting processing, ranging from individual tasks such as speech front-end processing, speech recognition and speaker diarization, to multi-modality modeling and joint optimization of relevant tasks. We also release a PyTorch-based training and evaluation framework as a baseline system to promote reproducible research in this field. The baseline system code and generated samples are available here.

You can cite the data using the following BibTeX entry:

title={AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario},
author={Yihui Fu, Luyao Cheng, Shubo Lv, Yukai Jv, Yuxiang Kong, Zhuo Chen, Yanxin Hu, Lei Xie, Jian Wu, Hui Bu, Xin Xu, Jun Du, Jingdong Chen},

External URL:   Full description from the company website