# Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation

arXiv: https://arxiv.org/abs/2305.10951 · HTML: https://mlsanity.com/paper/2305.10951/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 18 May 2023.

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

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Self-Training for End-to-End Speech Recognition](https://mlsanity.com/paper/1909.09116/) (2019)
- [Self-training and Pre-training are Complementary for Speech Recognition](https://mlsanity.com/paper/2010.11430/) (2020)
- [Iterative Pseudo-Labeling for Speech Recognition](https://mlsanity.com/paper/2005.09267/) (2020)
- [Rapid Adaptation of Neural Machine Translation to New Languages](https://mlsanity.com/paper/1808.04189/) (2018)
- [Speech Recognition with Augmented Synthesized Speech](https://mlsanity.com/paper/1909.11699/) (2019)
- [Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems](https://mlsanity.com/paper/1912.09257/) (2019)
- [Pseudo-Labeling for Massively Multilingual Speech Recognition](https://mlsanity.com/paper/2111.00161/) (2021)
- [Adapting Multilingual Speech Representation Model for a New, Underresourced Language through Multilingual Fine-tuning and Continued Pretraining](https://mlsanity.com/paper/2301.07295/) (2023)
- [Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data](https://mlsanity.com/paper/2205.08014/) (2022)
- [Neural Representations for Modeling Variation in Speech](https://mlsanity.com/paper/2011.12649/) (2020)
- [Voice Conversion Can Improve ASR in Very Low-Resource Settings](https://mlsanity.com/paper/2111.02674/) (2021)
- [Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0](https://mlsanity.com/paper/2110.03560/) (2021)
- [Quantifying Language Variation Acoustically with Few Resources](https://mlsanity.com/paper/2205.02694/) (2022)
- [Continuous Pseudo-Labeling from the Start](https://mlsanity.com/paper/2210.08711/) (2022)
- [Wav2Vec-Aug: Improved self-supervised training with limited data](https://mlsanity.com/paper/2206.13654/) (2022)
- [Leveraging pre-trained representations to improve access to untranscribed speech from endangered languages](https://mlsanity.com/paper/2103.14583/) (2021)
- [When Is TTS Augmentation Through a Pivot Language Useful?](https://mlsanity.com/paper/2207.09889/) (2022)
- [Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems A case study for Modern Greek](https://mlsanity.com/paper/2301.00304/) (2022)
- [Leveraging supplementary text data to kick-start automatic speech recognition system development with limited transcriptions](https://mlsanity.com/paper/2302.04975/) (2023)
- [Automated speech tools for helping communities process restricted-access corpora for language revival efforts](https://mlsanity.com/paper/2204.07272/) (2022)

## Built on by (most cited first, 30 of 31)

- [Can Whisper perform speech-based in-context learning?](https://mlsanity.com/paper/2309.07081/) (2023)
- [Casablanca: Data and Models for Multidialectal Arabic Speech Recognition](https://mlsanity.com/paper/2410.04527/) (2024)
- [Learn and Don't Forget: Adding a New Language to ASR Foundation Models](https://mlsanity.com/paper/2407.06800/) (2024)
- [SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models](https://mlsanity.com/paper/2506.12935/) (2025)
- [Task Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognition](https://mlsanity.com/paper/2406.02925/) (2024)
- [Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap](https://mlsanity.com/paper/2410.16726/) (2024)
- [The Zeno's Paradox of `Low-Resource' Languages](https://mlsanity.com/paper/2410.20817/) (2024)
- [Predicting positive transfer for improved low-resource speech recognition using acoustic pseudo-tokens](https://mlsanity.com/paper/2402.02302/) (2024)
- [Data Quality Issues in Multilingual Speech Datasets: The Need for Sociolinguistic Awareness and Proactive Language Planning](https://mlsanity.com/paper/2506.17525/) (2025)
- [Improving child speech recognition with augmented child-like speech](https://mlsanity.com/paper/2406.10284/) (2024)
- [What is "Typological Diversity" in NLP?](https://mlsanity.com/paper/2402.04222/) (2024)
- [Pre-training with Synthetic Patterns for Audio](https://mlsanity.com/paper/2410.00511/) (2024)
- [Speaker-Aware Simulation Improves Conversational Speech Recognition](https://mlsanity.com/paper/2602.04776/) (2026)
- [CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models](https://mlsanity.com/paper/2404.02408/) (2024)
- [Contrastive Learning from Synthetic Audio Doppelg"angers](https://mlsanity.com/paper/2406.05923/) (2024)
- [ManWav: The First Manchu ASR Model](https://mlsanity.com/paper/2406.13502/) (2024)
- [Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper](https://mlsanity.com/paper/2409.13499/) (2024)
- [Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC](https://mlsanity.com/paper/2505.24200/) (2025)
- [A Deep Learning Automatic Speech Recognition Model for Shona Language](https://mlsanity.com/paper/2507.21331/) (2025)
- [Doing More with Less: Data Augmentation for Sudanese Dialect Automatic Speech Recognition](https://mlsanity.com/paper/2601.06802/) (2026)
- [TI-ASU: Toward Robust Automatic Speech Understanding through Text-to-speech Imputation Against Missing Speech Modality](https://mlsanity.com/paper/2404.17983/) (2024)
- [Exploring the Impact of Data Quantity on ASR in Extremely Low-resource Languages](https://mlsanity.com/paper/2409.08872/) (2024)
- [Methods to Increase the Amount of Data for Speech Recognition for Low Resource Languages](https://mlsanity.com/paper/2501.14788/) (2025)
- [Weak Supervision Techniques towards Enhanced ASR Models in Industry-level CRM Systems](https://mlsanity.com/paper/2507.16843/) (2025)
- [Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens](https://mlsanity.com/paper/2511.03880/) (2025)
- [Elderly-Contextual Data Augmentation via Speech Synthesis for Elderly ASR](https://mlsanity.com/paper/2604.24770/) (2026)
- [Can Large Language Models Reliably Correct Errors in Low-Resource ASR? A Contamination-Aware Case Study on West Frisian](https://mlsanity.com/paper/2605.19711/) (2026)
- [Synthetic Speech, Real Signal: Paralinguistic Preservation and Cross-Lingual Augmentation via Voice Cloning](https://mlsanity.com/paper/2607.22304/) (2026)
- [Phoneme-guided TTS augmentation for ASR: A unified pipeline and multilingual evaluation](https://mlsanity.com/paper/2608.26697/) (2026)
- [T-SANDHI: Tone Sandhi-aware Adaptive Network with Decoupled Hybrid Injection for Low-resource Taiwanese Hokkien Speech Recognition](https://mlsanity.com/paper/2609.18194/) (2026)

## Abstract (arXiv)

The performance of automatic speech recognition (ASR) systems has advanced substantially in recent years, particularly for languages for which a large amount of transcribed speech is available. Unfortunately, for low-resource languages, such as minority languages, regional languages or dialects, ASR performance generally remains much lower. In this study, we investigate whether data augmentation techniques could help improve low-resource ASR performance, focusing on four typologically diverse minority languages or language variants (West Germanic: Gronings, West-Frisian; Malayo-Polynesian: Besemah, Nasal). For all four languages, we examine the use of self-training, where an ASR system trained with the available human-transcribed data is used to generate transcriptions, which are then combined with the original data to train a new ASR system. For Gronings, for which there was a pre-existing text-to-speech (TTS) system available, we also examined the use of TTS to generate ASR training data from text-only sources. We find that using a self-training approach consistently yields improved performance (a relative WER reduction up to 20.5% compared to using an ASR system trained on 24 minutes of manually transcribed speech). The performance gain from TTS augmentation for Gronings was even stronger (up to 25.5% relative reduction in WER compared to a system based on 24 minutes of manually transcribed speech). In sum, our results show the benefit of using self-training or (if possible) TTS-generated data as an efficient solution to overcome the limitations of data availability for resource-scarce languages in order to improve ASR performance.