# Monolingual Recognizers Fusion for Code-switching Speech Recognition

arXiv: https://arxiv.org/abs/2211.01046 · HTML: https://mlsanity.com/paper/2211.01046/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition](https://mlsanity.com/paper/2110.03370/) (2021)
- [The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results](https://mlsanity.com/paper/2007.05916/) (2020)
- [On the End-to-End Solution to Mandarin-English Code-switching Speech Recognition](https://mlsanity.com/paper/1811.00241/) (2018)
- [U2++: Unified Two-pass Bidirectional End-to-end Model for Speech Recognition](https://mlsanity.com/paper/2106.05642/) (2021)
- [Multi-Encoder-Decoder Transformer for Code-Switching Speech Recognition](https://mlsanity.com/paper/2006.10414/) (2020)
- [Transformer-Transducers for Code-Switched Speech Recognition](https://mlsanity.com/paper/2011.15023/) (2020)
- [Meta-Transfer Learning for Code-Switched Speech Recognition](https://mlsanity.com/paper/2004.14228/) (2020)
- [LAE: Language-Aware Encoder for Monolingual and Multilingual ASR](https://mlsanity.com/paper/2206.02093/) (2022)
- [Language-specific Characteristic Assistance for Code-switching Speech Recognition](https://mlsanity.com/paper/2206.14580/) (2022)
- [Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization](https://mlsanity.com/paper/2111.15016/) (2021)
- [Mandarin-English Code-switching Speech Recognition with Self-supervised Speech Representation Models](https://mlsanity.com/paper/2110.03504/) (2021)
- [Constrained Output Embeddings for End-to-End Code-Switching Speech Recognition with Only Monolingual Data](https://mlsanity.com/paper/1904.03802/) (2019)
- [Decoupling Pronunciation and Language for End-to-end Code-switching Automatic Speech Recognition](https://mlsanity.com/paper/2010.14798/) (2020)

## Built on by (most cited first, 2 of 2)

- [BA-MoE: Boundary-Aware Mixture-of-Experts Adapter for Code-Switching Speech Recognition](https://mlsanity.com/paper/2310.02629/) (2023)
- [Adapting Whisper for Code-Switching through Encoding Refining and Language-Aware Decoding](https://mlsanity.com/paper/2412.16507/) (2024)

## Abstract (arXiv)

The bi-encoder structure has been intensively investigated in code-switching (CS) automatic speech recognition (ASR). However, most existing methods require the structures of two monolingual ASR models (MAMs) should be the same and only use the encoder of MAMs. This leads to the problem that pre-trained MAMs cannot be timely and fully used for CS ASR. In this paper, we propose a monolingual recognizers fusion method for CS ASR. It has two stages: the speech awareness (SA) stage and the language fusion (LF) stage. In the SA stage, acoustic features are mapped to two language-specific predictions by two independent MAMs. To keep the MAMs focused on their own language, we further extend the language-aware training strategy for the MAMs. In the LF stage, the BELM fuses two language-specific predictions to get the final prediction. Moreover, we propose a text simulation strategy to simplify the training process of the BELM and reduce reliance on CS data. Experiments on a Mandarin-English corpus show the efficiency of the proposed method. The mix error rate is significantly reduced on the test set after using open-source pre-trained MAMs.