# TALCS: An Open-Source Mandarin-English Code-Switching Corpus and a Speech Recognition Baseline

arXiv: https://arxiv.org/abs/2206.13135 · HTML: https://mlsanity.com/paper/2206.13135/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 27 Jun 2022.

## Builds on (most cited first, 13 of 13)

- [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)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [Attention Augmented Convolutional Networks](https://mlsanity.com/paper/1904.09925/) (2019)
- [WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition](https://mlsanity.com/paper/2110.03370/) (2021)
- [WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit](https://mlsanity.com/paper/2102.01547/) (2021)
- [The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage](https://mlsanity.com/paper/2111.09344/) (2021)
- [The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results](https://mlsanity.com/paper/2007.05916/) (2020)
- [Towards End-to-End Code-Switching Speech Recognition](https://mlsanity.com/paper/1810.13091/) (2018)
- [Learn to Code-Switch: Data Augmentation using Copy Mechanism on Language Modeling](https://mlsanity.com/paper/1810.10254/) (2018)
- [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, 17 of 17)

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding](https://mlsanity.com/paper/2406.15481/) (2024)
- [MSR-86K: An Evolving, Multilingual Corpus with 86,300 Hours of Transcribed Audio for Speech Recognition Research](https://mlsanity.com/paper/2406.18301/) (2024)
- [CroCoSum: A Benchmark Dataset for Cross-Lingual Code-Switched Summarization](https://mlsanity.com/paper/2303.04092/) (2023)
- [Weight Factorization and Centralization for Continual Learning in Speech Recognition](https://mlsanity.com/paper/2506.16574/) (2025)
- [A Multitask Training Approach to Enhance Whisper with Contextual Biasing and Open-Vocabulary Keyword Spotting](https://mlsanity.com/paper/2309.09552/) (2023)
- [Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalities](https://mlsanity.com/paper/2510.07037/) (2025)
- [Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review](https://mlsanity.com/paper/2507.07741/) (2025)
- [HiKE: Hierarchical Evaluation Framework for Korean-English Code-Switching Speech Recognition](https://mlsanity.com/paper/2509.24613/) (2025)
- [Summary on the ISCSLP 2022 Chinese-English Code-Switching ASR Challenge](https://mlsanity.com/paper/2210.06091/) (2022)
- [Towards Comprehensive Semantic Speech Embeddings for Chinese Dialects](https://mlsanity.com/paper/2601.07274/) (2026)
- [MultiTalk: Scaling Full-Duplex Speech Models to Long, Multi-Party, Bilingual Conversation](https://mlsanity.com/paper/2609.36903/) (2026)
- [Leveraging Zipformer Model for Effective Language Identification in Code-Switched Child-Directed Speech](https://mlsanity.com/paper/2508.09430/) (2025)
- [Introducing MELI: the Mandarin-English Language Interview Corpus](https://mlsanity.com/paper/2603.27043/) (2026)
- [RAS: a Reliability Oriented Metric for Automatic Speech Recognition](https://mlsanity.com/paper/2604.24278/) (2026)
- [Read What You Hear: Reference-Free Hypotheses Evaluation with Acoustic Discrepancy](https://mlsanity.com/paper/2606.04680/) (2026)
- [Towards Truly Multilingual ASR: Generalizing Code-Switching ASR to Unseen Language Pairs](https://mlsanity.com/paper/2606.05846/) (2026)

## Abstract (arXiv)

This paper introduces a new corpus of Mandarin-English code-switching speech recognition--TALCS corpus, suitable for training and evaluating code-switching speech recognition systems. TALCS corpus is derived from real online one-to-one English teaching scenes in TAL education group, which contains roughly 587 hours of speech sampled at 16 kHz. To our best knowledge, TALCS corpus is the largest well labeled Mandarin-English code-switching open source automatic speech recognition (ASR) dataset in the world. In this paper, we will introduce the recording procedure in detail, including audio capturing devices and corpus environments. And the TALCS corpus is freely available for download under the permissive license1. Using TALCS corpus, we conduct ASR experiments in two popular speech recognition toolkits to make a baseline system, including ESPnet and Wenet. The Mixture Error Rate (MER) performance in the two speech recognition toolkits is compared in TALCS corpus. The experimental results implies that the quality of audio recordings and transcriptions are promising and the baseline system is workable.