Speech recognition in mixed language has difficulties to adapt end-to-end framework due to the lack of data and overlapping phone sets, for example in words such as” one” in English and” wàn” in Chinese. We propose a CTC-based end-to-end automatic speech recognition model for intra-sentential English-Mandarin code-switching. The model is trained by joint training on monolingual datasets, and fine-tuning with the mixed-language corpus. During the decoding process, we apply a beam search and combine CTC predictions and language model score. The proposed method is effective in leveraging monolingual corpus and detecting language transitions and it improves the CER by 5%.
Recommended citation: Winata, G. I., Madotto, A., Wu, C. S., & Fung, P. (2018). Towards end-to-end automatic code-switching speech recognition. arXiv preprint arXiv:1810.12620.