  Code-switching automatic speech recognition becomes one of the most
challenging and the most valuable scenarios of automatic speech recognition,
due to the code-switching phenomenon between multilingual language and the
frequent occurrence of code-switching phenomenon in daily life. The ISCSLP 2022
Chinese-English Code-Switching Automatic Speech Recognition (CSASR) Challenge
aims to promote the development of code-switching automatic speech recognition.
The ISCSLP 2022 CSASR challenge provided two training sets, TAL_CSASR corpus
and MagicData-RAMC corpus, a development and a test set for participants, which
are used for CSASR model training and evaluation. Along with the challenge, we
also provide the baseline system performance for reference. As a result, more
than 40 teams participated in this challenge, and the winner team achieved
16.70% Mixture Error Rate (MER) performance on the test set and has achieved
9.8% MER absolute improvement compared with the baseline system. In this paper,
we will describe the datasets, the associated baselines system and the
requirements, and summarize the CSASR challenge results and major techniques
and tricks used in the submitted systems.
