# Learning Invariant Representation and Risk Minimized for Unsupervised Accent Domain Adaptation

arXiv: https://arxiv.org/abs/2210.08182 · HTML: https://mlsanity.com/paper/2210.08182/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 15 Oct 2022.

## Builds on (most cited first, 20 of 20)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations](https://mlsanity.com/paper/1910.05453/) (2019)
- [Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders](https://mlsanity.com/paper/1910.12638/) (2019)
- [Unsupervised Speech Recognition](https://mlsanity.com/paper/2105.11084/) (2021)
- [Multi-task self-supervised learning for Robust Speech Recognition](https://mlsanity.com/paper/2001.09239/) (2020)
- [Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection](https://mlsanity.com/paper/2005.14140/) (2020)
- [Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data](https://mlsanity.com/paper/1709.07902/) (2017)
- [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://mlsanity.com/paper/2101.07597/) (2021)
- [wav2letter++: The Fastest Open-source Speech Recognition System](https://mlsanity.com/paper/1812.07625/) (2018)
- [Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training](https://mlsanity.com/paper/2011.13439/) (2020)
- [Semi-Supervised Spoken Language Understanding via Self-Supervised Speech and Language Model Pretraining](https://mlsanity.com/paper/2010.13826/) (2020)
- [Learning Latent Representations for Speech Generation and Transformation](https://mlsanity.com/paper/1704.04222/) (2017)
- [A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning](https://mlsanity.com/paper/2006.02547/) (2020)
- [Federated Learning with Dynamic Transformer for Text to Speech](https://mlsanity.com/paper/2107.08795/) (2021)
- [A Comparison of Discrete Latent Variable Models for Speech Representation Learning](https://mlsanity.com/paper/2010.14230/) (2020)
- [r-G2P: Evaluating and Enhancing Robustness of Grapheme to Phoneme Conversion by Controlled noise introducing and Contextual information incorporation](https://mlsanity.com/paper/2202.11194/) (2022)
- [Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition](https://mlsanity.com/paper/2209.15176/) (2022)

## Abstract (arXiv)

Unsupervised representation learning for speech audios attained impressive performances for speech recognition tasks, particularly when annotated speech is limited. However, the unsupervised paradigm needs to be carefully designed and little is known about what properties these representations acquire. There is no guarantee that the model learns meaningful representations for valuable information for recognition. Moreover, the adaptation ability of the learned representations to other domains still needs to be estimated. In this work, we explore learning domain-invariant representations via a direct mapping of speech representations to their corresponding high-level linguistic informations. Results prove that the learned latents not only capture the articulatory feature of each phoneme but also enhance the adaptation ability, outperforming the baseline largely on accented benchmarks.