# Improving Code-Switching Speech Recognition with TTS Data Augmentation

arXiv: https://arxiv.org/abs/2601.00935 · HTML: https://mlsanity.com/paper/2601.00935/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 2 Jan 2026.

## Builds on (most cited first, 12 of 12)

- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022](https://mlsanity.com/paper/2204.02152/) (2022)
- [CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokens](https://mlsanity.com/paper/2407.05407/) (2024)
- [Mamba in Speech: Towards an Alternative to Self-Attention](https://mlsanity.com/paper/2405.12609/) (2024)
- [Reducing Language confusion for Code-switching Speech Recognition with Token-level Language Diarization](https://mlsanity.com/paper/2210.14567/) (2022)
- [Enhancing Code-switching Speech Recognition with Interactive Language Biases](https://mlsanity.com/paper/2309.16953/) (2023)
- [Aligning Speech to Languages to Enhance Code-switching Speech Recognition](https://mlsanity.com/paper/2403.05887/) (2024)
- [Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap](https://mlsanity.com/paper/2410.16726/) (2024)
- [Speech collage: code-switched audio generation by collaging monolingual corpora](https://mlsanity.com/paper/2309.15674/) (2023)
- [A Self-Refining Framework for Enhancing ASR Using TTS-Synthesized Data](https://mlsanity.com/paper/2506.11130/) (2025)
- [Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model](https://mlsanity.com/paper/2402.10642/) (2024)
- [Can we train ASR systems on Code-switch without real code-switch data? Case study for Singapore's languages](https://mlsanity.com/paper/2506.14177/) (2025)

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

- [Improving Code-Switching ASR with Code-Mixing Guided Synthetic Speech](https://mlsanity.com/paper/2606.19381/) (2026)
- [Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models](https://mlsanity.com/paper/2610.18726/) (2026)

## Abstract (arXiv)

Automatic speech recognition (ASR) for conversational code-switching speech remains challenging due to the scarcity of realistic, high-quality labeled speech data. This paper explores multilingual text-to-speech (TTS) models as an effective data augmentation technique to address this shortage. Specifically, we fine-tune the multilingual CosyVoice2 TTS model on the SEAME dataset to generate synthetic conversational Chinese-English code-switching speech, significantly increasing the quantity and speaker diversity of available training data. Our experiments demonstrate that augmenting real speech with synthetic speech reduces the mixed error rate (MER) from 12.1 percent to 10.1 percent on DevMan and from 17.8 percent to 16.0 percent on DevSGE, indicating consistent performance gains. These results confirm that multilingual TTS is an effective and practical tool for enhancing ASR robustness in low-resource conversational code-switching scenarios.