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

• URL: http://arxiv.org/abs/2206.13135v1
• Date: Mon, 27 Jun 2022 09:30:25 GMT
• Title: TALCS: An Open-Source Mandarin-English Code-Switching Corpus and a Speech Recognition Baseline
• Authors: Chengfei Li, Shuhao Deng, Yaoping Wang, Guangjing Wang, Yaguang Gong, Changbin Chen and Jinfeng Bai
• Abstract summary: This paper introduces a new corpus of Mandarin-English code-switching speech recognition--TALCS corpus. TALCS corpus is derived from real online one-to-one English teaching scenes in TAL education group. To our best knowledge, TALCS corpus is the largest well labeled Mandarin-English code-switching open source automatic speech recognition dataset in the world.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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