# Non-autoregressive Mandarin-English Code-switching Speech Recognition

arXiv: https://arxiv.org/abs/2104.02258 · HTML: https://mlsanity.com/paper/2104.02258/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [A Comparative Study on Transformer vs RNN in Speech Applications](https://mlsanity.com/paper/1909.06317/) (2019)
- [Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict](https://mlsanity.com/paper/2005.08700/) (2020)
- [Imputer: Sequence Modelling via Imputation and Dynamic Programming](https://mlsanity.com/paper/2002.08926/) (2020)
- [Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment](https://mlsanity.com/paper/2010.14233/) (2020)
- [Improved Mask-CTC for Non-Autoregressive End-to-End ASR](https://mlsanity.com/paper/2010.13270/) (2020)
- [On the End-to-End Solution to Mandarin-English Code-switching Speech Recognition](https://mlsanity.com/paper/1811.00241/) (2018)
- [Multi-Encoder-Decoder Transformer for Code-Switching Speech Recognition](https://mlsanity.com/paper/2006.10414/) (2020)
- [Improving RNN Transducer Based ASR with Auxiliary Tasks](https://mlsanity.com/paper/2011.03109/) (2020)
- [Transformer-Transducers for Code-Switched Speech Recognition](https://mlsanity.com/paper/2011.15023/) (2020)
- [Code-switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation](https://mlsanity.com/paper/1811.02356/) (2018)
- [Data Augmentation for End-to-end Code-switching Speech Recognition](https://mlsanity.com/paper/2011.02160/) (2020)
- [Improving Low Resource Code-switched ASR using Augmented Code-switched TTS](https://mlsanity.com/paper/2010.05549/) (2020)
- [Rnn-transducer with language bias for end-to-end Mandarin-English code-switching speech recognition](https://mlsanity.com/paper/2002.08126/) (2020)
- [Training a code-switching language model with monolingual data](https://mlsanity.com/paper/1911.06003/) (2019)
- [Reducing Spelling Inconsistencies in Code-Switching ASR using Contextualized CTC Loss](https://mlsanity.com/paper/2005.07920/) (2020)

## Built on by (most cited first, 8 of 8)

- [A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond](https://mlsanity.com/paper/2204.09269/) (2022)
- [Code-Switching Text Generation and Injection in Mandarin-English ASR](https://mlsanity.com/paper/2303.10949/) (2023)
- [Mandarin-English Code-switching Speech Recognition with Self-supervised Speech Representation Models](https://mlsanity.com/paper/2110.03504/) (2021)
- [LAE-ST-MoE: Boosted Language-Aware Encoder Using Speech Translation Auxiliary Task for E2E Code-switching ASR](https://mlsanity.com/paper/2309.16178/) (2023)
- [Leave No Knowledge Behind During Knowledge Distillation: Towards Practical and Effective Knowledge Distillation for Code-Switching ASR Using Realistic Data](https://mlsanity.com/paper/2407.10603/) (2024)
- [Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review](https://mlsanity.com/paper/2507.07741/) (2025)
- [Romanization Encoding For Multilingual ASR](https://mlsanity.com/paper/2407.04368/) (2024)
- [Multi-Level Modeling Units for End-to-End Mandarin Speech Recognition](https://mlsanity.com/paper/2205.11998/) (2022)

## Abstract (arXiv)

Mandarin-English code-switching (CS) is frequently used among East and Southeast Asian people. However, the intra-sentence language switching of the two very different languages makes recognizing CS speech challenging. Meanwhile, the recent successful non-autoregressive (NAR) ASR models remove the need for left-to-right beam decoding in autoregressive (AR) models and achieved outstanding performance and fast inference speed, but it has not been applied to Mandarin-English CS speech recognition. This paper takes advantage of the Mask-CTC NAR ASR framework to tackle the CS speech recognition issue. We further propose to change the Mandarin output target of the encoder to Pinyin for faster encoder training and introduce the Pinyin-to-Mandarin decoder to learn contextualized information. Moreover, we use word embedding label smoothing to regularize the decoder with contextualized information and projection matrix regularization to bridge that gap between the encoder and decoder. We evaluate these methods on the SEAME corpus and achieved exciting results.