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.
翻译:密码转换自动语音识别由于多语种语言之间的代码转换现象和日常生活中经常出现的代码转换现象,成为自动语音识别最困难和最有价值的情景之一; ISSCLP 2022 中文-英文代码转换自动语音识别(CSASR)挑战,旨在促进开发代码转换自动语音识别; ISSCLP 2022 CSASR挑战提供了两套培训,即TAL_CSASR文具和MagicData-RAMCC文具,一套针对参与者的开发和测试套,用于CSASR模型培训和评估; 除了这项挑战外,我们还提供基准系统性能参考,40多个团队参加了这项挑战,优胜者团队在测试集上实现了16.70%的像素调率,并与基线系统相比实现了9.8%的MER绝对改进; 在本文件中,我们将介绍数据集、相关基线系统和要求,并概述CSASR挑战的结果以及所提交的系统中使用的主要技术和技巧。