  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 tasks: 1) Task 1 is multi-channel speech
enhancement with single microphone array and focusing on practical application
with real-time requirement and 2) Task 2 is multi-channel speech enhancement
with multiple distributed microphone arrays, which is a non-real-time track and
does not have any constraints so that participants could explore any algorithms
to obtain high speech quality. Targeting the real video conferencing room
application, the challenge database was recorded from real speakers and all
recording facilities were located by following the real setup of conferencing
room. In this challenge, we open-sourced the list of open source clean speech
and noise datasets, simulation scripts, and a baseline system for participants
to develop their own system. The final ranking of the challenge will be decided
by the subjective evaluation which is performed using Absolute Category Ratings
(ACR) to estimate Mean Opinion Score (MOS), speech MOS (S-MOS), and noise MOS
(N-MOS). This paper describes the challenge, tasks, datasets, and subjective
evaluation. The baseline system which is a complex ratio mask based neural
network and its experimental results are also presented.
