# Towards End-to-end Automatic Code-Switching Speech Recognition

arXiv: https://arxiv.org/abs/1810.12620 · HTML: https://mlsanity.com/paper/1810.12620/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2018.

## Built on by (most cited first, 4 of 4)

- [Investigations on Speech Recognition Systems for Low-Resource Dialectal Arabic-English Code-Switching Speech](https://mlsanity.com/paper/2108.12881/) (2021)
- [Multilingual self-supervised speech representations improve the speech recognition of low-resource African languages with codeswitching](https://mlsanity.com/paper/2311.15077/) (2023)
- [Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling](https://mlsanity.com/paper/2104.06268/) (2021)
- [Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit](https://mlsanity.com/paper/2308.08807/) (2023)

## Abstract (arXiv)

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 "w`an" 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%.