The CHiME challenges have played a significant role in the development and
evaluation of robust speech recognition (ASR) systems. We introduce the CHiME-7
distant ASR (DASR) task, within the 7th CHiME challenge. This task comprises
joint ASR and diarization in far-field settings with multiple, and possibly
heterogeneous, recording devices. Different from previous challenges, we
evaluate systems on 3 diverse scenarios: CHiME-6, DiPCo, and Mixer 6. The goal
is for participants to devise a single system that can generalize across
different array geometries and use cases with no a-priori information. Another
departure from earlier CHiME iterations is that participants are allowed to use
open-source pre-trained models and datasets. In this paper, we describe the
challenge design, motivation, and fundamental research questions in detail. We
also present the baseline system, which is fully array-topology agnostic and
features multi-channel diarization, channel selection, guided source separation
and a robust ASR model that leverages self-supervised speech representations
(SSLR).

本文介绍了第七届 CHiME 挑战赛中的远场 ASR (DASR) 任务，并描述了挑战的设计、动机和基本研究问题，该任务旨在通过使用自我监督的语音表示法（SSLR）来设计一种可以跨越不同阵列几何和用例的单一系统。