# Study of Semi-supervised Approaches to Improving English-Mandarin Code-Switching Speech Recognition

arXiv: https://arxiv.org/abs/1806.06200 · HTML: https://mlsanity.com/paper/1806.06200/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2018.

## Built on by (most cited first, 7 of 7)

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [BA-MoE: Boundary-Aware Mixture-of-Experts Adapter for Code-Switching Speech Recognition](https://mlsanity.com/paper/2310.02629/) (2023)
- [Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition](https://mlsanity.com/paper/2210.08992/) (2022)
- [Internal Language Model Estimation based Language Model Fusion for Cross-Domain Code-Switching Speech Recognition](https://mlsanity.com/paper/2207.04176/) (2022)
- [Enhancing Code-Switching Speech Recognition with LID-Based Collaborative Mixture of Experts Model](https://mlsanity.com/paper/2409.02050/) (2024)
- [Using heterogeneity in semi-supervised transcription hypotheses to improve code-switched speech recognition](https://mlsanity.com/paper/2106.07699/) (2021)
- [Pronunciation Generation for Foreign Language Words in Intra-Sentential Code-Switching Speech Recognition](https://mlsanity.com/paper/2210.14691/) (2022)

## Abstract (arXiv)

In this paper, we present our overall efforts to improve the performance of a code-switching speech recognition system using semi-supervised training methods from lexicon learning to acoustic modeling, on the South East Asian Mandarin-English (SEAME) data. We first investigate semi-supervised lexicon learning approach to adapt the canonical lexicon, which is meant to alleviate the heavily accented pronunciation issue within the code-switching conversation of the local area. As a result, the learned lexicon yields improved performance. Furthermore, we attempt to use semi-supervised training to deal with those transcriptions that are highly mismatched between human transcribers and ASR system. Specifically, we conduct semi-supervised training assuming those poorly transcribed data as unsupervised data. We found the semi-supervised acoustic modeling can lead to improved results. Finally, to make up for the limitation of the conventional n-gram language models due to data sparsity issue, we perform lattice rescoring using neural network language models, and significant WER reduction is obtained.