  This paper introduces the sixth Oriental Language Recognition (OLR) 2021
Challenge, which intends to improve the performance of language recognition
systems and speech recognition systems within multilingual scenarios. The data
profile, four tasks, two baselines, and the evaluation principles are
introduced in this paper. In addition to the Language Identification (LID)
tasks, multilingual Automatic Speech Recognition (ASR) tasks are introduced to
OLR 2021 Challenge for the first time. The challenge this year focuses on more
practical and challenging problems, with four tasks: (1) constrained LID, (2)
unconstrained LID, (3) constrained multilingual ASR, (4) unconstrained
multilingual ASR. Baselines for LID tasks and multilingual ASR tasks are
provided, respectively. The LID baseline system is an extended TDNN x-vector
model constructed with Pytorch. A transformer-based end-to-end model is
provided as the multilingual ASR baseline system. These recipes will be online
published, and available for participants to construct their own LID or ASR
systems. The baseline results demonstrate that those tasks are rather
challenging and deserve more effort to achieve better performance.
