# Speech collage: code-switched audio generation by collaging monolingual corpora

arXiv: https://arxiv.org/abs/2309.15674 · HTML: https://mlsanity.com/paper/2309.15674/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 40 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Builds on (most cited first, 23 of 23)

- [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)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation](https://mlsanity.com/paper/1805.04699/) (2018)
- [Word Alignment by Fine-tuning Embeddings on Parallel Corpora](https://mlsanity.com/paper/2101.08231/) (2021)
- [Recent Developments on ESPnet Toolkit Boosted by Conformer](https://mlsanity.com/paper/2010.13956/) (2020)
- [A Survey of Code-switched Speech and Language Processing](https://mlsanity.com/paper/1904.00784/) (2019)
- [Speech Recognition with Augmented Synthesized Speech](https://mlsanity.com/paper/1909.11699/) (2019)
- [Towards One Model to Rule All: Multilingual Strategy for Dialectal Code-Switching Arabic ASR](https://mlsanity.com/paper/2105.14779/) (2021)
- [Lhotse: a speech data representation library for the modern deep learning ecosystem](https://mlsanity.com/paper/2110.12561/) (2021)
- [Multi-Encoder-Decoder Transformer for Code-Switching Speech Recognition](https://mlsanity.com/paper/2006.10414/) (2020)
- [On Addressing Practical Challenges for RNN-Transducer](https://mlsanity.com/paper/2105.00858/) (2021)
- [Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization](https://mlsanity.com/paper/2111.15016/) (2021)
- [Improving Performance of End-to-End ASR on Numeric Sequences](https://mlsanity.com/paper/1907.01372/) (2019)
- [Arabic Code-Switching Speech Recognition using Monolingual Data](https://mlsanity.com/paper/2107.01573/) (2021)
- [Improving Low Resource Code-switched ASR using Augmented Code-switched TTS](https://mlsanity.com/paper/2010.05549/) (2020)
- [Towards Zero-Shot Code-Switched Speech Recognition](https://mlsanity.com/paper/2211.01458/) (2022)
- [Code-Switching Text Generation and Injection in Mandarin-English ASR](https://mlsanity.com/paper/2303.10949/) (2023)
- [Learning to Recognize Code-switched Speech Without Forgetting Monolingual Speech Recognition](https://mlsanity.com/paper/2006.00782/) (2020)
- [Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition](https://mlsanity.com/paper/2201.02550/) (2022)
- [On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR](https://mlsanity.com/paper/2104.01393/) (2021)
- [Training a code-switching language model with monolingual data](https://mlsanity.com/paper/1911.06003/) (2019)
- [Balanced End-to-End Monolingual pre-training for Low-Resourced Indic Languages Code-Switching Speech Recognition](https://mlsanity.com/paper/2106.05885/) (2021)

## Built on by (most cited first, 10 of 10)

- [A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2501.13419/) (2025)
- [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)
- [Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalities](https://mlsanity.com/paper/2510.07037/) (2025)
- [Code-Switching in End-to-End Automatic Speech Recognition: A Systematic Literature Review](https://mlsanity.com/paper/2507.07741/) (2025)
- [HiKE: Hierarchical Evaluation Framework for Korean-English Code-Switching Speech Recognition](https://mlsanity.com/paper/2509.24613/) (2025)
- [CAMEL: Cross-Attention Enhanced Mixture-of-Experts and Language Bias for Code-Switching Speech Recognition](https://mlsanity.com/paper/2412.12760/) (2024)
- [SAGE: Spliced-Audio Generated Data for Enhancing Foundational Models in Low-Resource Arabic-English Code-Switched Speech Recognition](https://mlsanity.com/paper/2506.22143/) (2025)
- [Improving Code-Switching Speech Recognition with TTS Data Augmentation](https://mlsanity.com/paper/2601.00935/) (2026)
- [Direct Preference Optimization for English-Mandarin Code-Switching Speech Recognition in Audio LLMs](https://mlsanity.com/paper/2605.23975/) (2026)
- [Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models](https://mlsanity.com/paper/2610.18726/) (2026)

## Abstract (arXiv)

Designing effective automatic speech recognition (ASR) systems for Code-Switching (CS) often depends on the availability of the transcribed CS resources. To address data scarcity, this paper introduces Speech Collage, a method that synthesizes CS data from monolingual corpora by splicing audio segments. We further improve the smoothness quality of audio generation using an overlap-add approach. We investigate the impact of generated data on speech recognition in two scenarios: using in-domain CS text and a zero-shot approach with synthesized CS text. Empirical results highlight up to 34.4% and 16.2% relative reductions in Mixed-Error Rate and Word-Error Rate for in-domain and zero-shot scenarios, respectively. Lastly, we demonstrate that CS augmentation bolsters the model's code-switching inclination and reduces its monolingual bias.