# Cross lingual transfer learning for zero-resource domain adaptation

arXiv: https://arxiv.org/abs/1910.02168 · HTML: https://mlsanity.com/paper/1910.02168/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [Measure and Improve Robustness in NLP Models: A Survey](https://mlsanity.com/paper/2112.08313/) (2021)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [Crowdsourcing with Enhanced Data Quality Assurance: An Efficient Approach to Mitigate Resource Scarcity Challenges in Training Large Language Models for Healthcare](https://mlsanity.com/paper/2405.13030/) (2024)
- [Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification](https://mlsanity.com/paper/2102.02074/) (2021)
- [Why Robust Natural Language Understanding is a Challenge](https://mlsanity.com/paper/2206.14575/) (2022)
- [Transliterated Zero-Shot Domain Adaptation for Automatic Speech Recognition](https://mlsanity.com/paper/2412.11185/) (2024)

## Abstract (arXiv)

We propose a method for zero-resource domain adaptation of DNN acoustic models, for use in low-resource situations where the only in-language training data available may be poorly matched to the intended target domain. Our method uses a multi-lingual model in which several DNN layers are shared between languages. This architecture enables domain adaptation transforms learned for one well-resourced language to be applied to an entirely different low-resource language. First, to develop the technique we use English as a well-resourced language and take Spanish to mimic a low-resource language. Experiments in domain adaptation between the conversational telephone speech (CTS) domain and broadcast news (BN) domain demonstrate a 29% relative WER improvement on Spanish BN test data by using only English adaptation data. Second, we demonstrate the effectiveness of the method for low-resource languages with a poor match to the well-resourced language. Even in this scenario, the proposed method achieves relative WER improvements of 18-27% by using solely English data for domain adaptation. Compared to other related approaches based on multi-task and multi-condition training, the proposed method is able to better exploit well-resource language data for improved acoustic modelling of the low-resource target domain.