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

arXiv: https://arxiv.org/abs/2210.06091 · HTML: https://mlsanity.com/paper/2210.06091/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Attention Augmented Convolutional Networks](https://mlsanity.com/paper/1904.09925/) (2019)
- [Squeezeformer: An Efficient Transformer for Automatic Speech Recognition](https://mlsanity.com/paper/2206.00888/) (2022)
- [SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts](https://mlsanity.com/paper/2105.03036/) (2021)
- [The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results](https://mlsanity.com/paper/2007.05916/) (2020)
- [Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset](https://mlsanity.com/paper/2203.16844/) (2022)
- [LAE: Language-Aware Encoder for Monolingual and Multilingual ASR](https://mlsanity.com/paper/2206.02093/) (2022)
- [Towards End-to-End Code-Switching Speech Recognition](https://mlsanity.com/paper/1810.13091/) (2018)
- [TALCS: An Open-Source Mandarin-English Code-Switching Corpus and a Speech Recognition Baseline](https://mlsanity.com/paper/2206.13135/) (2022)
- [Syntactic and Semantic Features For Code-Switching Factored Language Models](https://mlsanity.com/paper/1710.01809/) (2017)
- [Monolingual Data Selection Analysis for English-Mandarin Hybrid Code-switching Speech Recognition](https://mlsanity.com/paper/2006.07094/) (2020)

## Built on by (most cited first, 1 of 1)

- [Towards Zero-Shot Code-Switched Speech Recognition](https://mlsanity.com/paper/2211.01458/) (2022)

## Abstract (arXiv)

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.