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Code-switching automatic speech recognition becomes one of the mostchallenging and the most valuable scenarios of automatic speech recognition,due to the code-switching phenomenon between multilingual language and thefrequent occurrence of code-switching phenomenon in daily life. The ISCSLP 2022Chinese-English Code-Switching Automatic Speech Recognition (CSASR) Challengeaims to promote the development of code-switching automatic speech recognition.The ISCSLP 2022 CSASR challenge provided two training sets, TAL_CSASR corpusand MagicData-RAMC corpus, a development and a test set for participants, whichare used for CSASR model training and evaluation. Along with the challenge, wealso provide the baseline system performance for reference. As a result, morethan 40 teams participated in this challenge, and the winner team achieved16.70% Mixture Error Rate (MER) performance on the test set and has achieved9.8% MER absolute improvement compared with the baseline system. In this paper,we will describe the datasets, the associated baselines system and therequirements, and summarize the CSASR challenge results and major techniquesand tricks used in the submitted systems.

Summary on the ISCSLP 2022 Chinese-English Code-Switching ASR Challenge