# Unsupervised Domain Adaptation Schemes for Building ASR in Low-resource Languages

arXiv: https://arxiv.org/abs/2109.05494 · HTML: https://mlsanity.com/paper/2109.05494/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 12 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [Multilingual and code-switching ASR challenges for low resource Indian languages](https://mlsanity.com/paper/2104.00235/) (2021)
- [Domain Adversarial for Acoustic Emotion Recognition](https://mlsanity.com/paper/1804.07690/) (2018)
- [Unsupervised Adaptation with Domain Separation Networks for Robust Speech Recognition](https://mlsanity.com/paper/1711.08010/) (2017)
- [Adversarial Learning of Raw Speech Features for Domain Invariant Speech Recognition](https://mlsanity.com/paper/1805.08615/) (2018)

## Built on by (most cited first, 9 of 9)

- [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://mlsanity.com/paper/2208.07422/) (2022)
- [PADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech Representations](https://mlsanity.com/paper/2203.16965/) (2022)
- [Boosting Cross-Domain Speech Recognition with Self-Supervision](https://mlsanity.com/paper/2206.09783/) (2022)
- [Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems A case study for Modern Greek](https://mlsanity.com/paper/2301.00304/) (2022)
- [Efficient Data Selection for Domain Adaptation of ASR Using Pseudo-Labels and Multi-Stage Filtering](https://mlsanity.com/paper/2506.03681/) (2025)
- [SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment](https://mlsanity.com/paper/2606.25821/) (2026)
- [MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR](https://mlsanity.com/paper/2505.24656/) (2025)
- [Towards Robust Speech Recognition for Jamaican Patois Music Transcription](https://mlsanity.com/paper/2507.16834/) (2025)
- [Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition](https://mlsanity.com/paper/2207.12248/) (2022)

## Abstract (arXiv)

Building an automatic speech recognition (ASR) system from scratch requires a large amount of annotated speech data, which is difficult to collect in many languages. However, there are cases where the low-resource language shares a common acoustic space with a high-resource language having enough annotated data to build an ASR. In such cases, we show that the domain-independent acoustic models learned from the high-resource language through unsupervised domain adaptation (UDA) schemes can enhance the performance of the ASR in the low-resource language. We use the specific example of Hindi in the source domain and Sanskrit in the target domain. We explore two architectures: i) domain adversarial training using gradient reversal layer (GRL) and ii) domain separation networks (DSN). The GRL and DSN architectures give absolute improvements of 6.71% and 7.32%, respectively, in word error rate over the baseline deep neural network model when trained on just 5.5 hours of data in the target domain. We also show that choosing a proper language (Telugu) in the source domain can bring further improvement. The results suggest that UDA schemes can be helpful in the development of ASR systems for low-resource languages, mitigating the hassle of collecting large amounts of annotated speech data.