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

TL;DR

This paper presents TALCS, the largest open-source Mandarin-English code-switching speech corpus, and establishes baseline speech recognition performance using ESPnet and Wenet toolkits, demonstrating its potential for advancing code-switching ASR research.

Contribution

Introduction of TALCS, the largest open-source Mandarin-English code-switching speech corpus, along with baseline ASR systems using popular toolkits for future research.

Findings

TALCS contains approximately 587 hours of speech data.

Baseline ASR systems achieved promising error rates.

The corpus is freely available for research and development.

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.