# Monolingual Data Selection Analysis for English-Mandarin Hybrid Code-switching Speech Recognition

arXiv: https://arxiv.org/abs/2006.07094 · HTML: https://mlsanity.com/paper/2006.07094/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [TALCS: An Open-Source Mandarin-English Code-Switching Corpus and a Speech Recognition Baseline](https://mlsanity.com/paper/2206.13135/) (2022)
- [LAE-ST-MoE: Boosted Language-Aware Encoder Using Speech Translation Auxiliary Task for E2E Code-switching ASR](https://mlsanity.com/paper/2309.16178/) (2023)
- [Internal Language Model Estimation based Language Model Fusion for Cross-Domain Code-Switching Speech Recognition](https://mlsanity.com/paper/2207.04176/) (2022)
- [Summary on the ISCSLP 2022 Chinese-English Code-Switching ASR Challenge](https://mlsanity.com/paper/2210.06091/) (2022)

## Abstract (arXiv)

In this paper, we conduct data selection analysis in building an English-Mandarin code-switching (CS) speech recognition (CSSR) system, which is aimed for a real CSSR contest in China. The overall training sets have three subsets, i.e., a code-switching data set, an English (LibriSpeech) and a Mandarin data set respectively. The code-switching data are Mandarin dominated. First of all, it is found using the overall data yields worse results, and hence data selection study is necessary. Then to exploit monolingual data, we find data matching is crucial. Mandarin data is closely matched with the Mandarin part in the code-switching data, while English data is not. However, Mandarin data only helps on those utterances that are significantly Mandarin-dominated. Besides, there is a balance point, over which more monolingual data will divert the CSSR system, degrading results. Finally, we analyze the effectiveness of combining monolingual data to train a CSSR system with the HMM-DNN hybrid framework. The CSSR system can perform within-utterance code-switch recognition, but it still has a margin with the one trained on code-switching data.