  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.
