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

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