# Speaker Adaptive Training using Model Agnostic Meta-Learning

arXiv: https://arxiv.org/abs/1910.10605 · HTML: https://mlsanity.com/paper/1910.10605/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [Meta Learning for Natural Language Processing: A Survey](https://mlsanity.com/paper/2205.01500/) (2022)
- [Speaker-adaptive Lip Reading with User-dependent Padding](https://mlsanity.com/paper/2208.04498/) (2022)
- [Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition](https://mlsanity.com/paper/2302.08102/) (2023)
- [Improved Meta Learning for Low Resource Speech Recognition](https://mlsanity.com/paper/2205.06182/) (2022)
- [Personalized Lip Reading: Adapting to Your Unique Lip Movements with Vision and Language](https://mlsanity.com/paper/2409.00986/) (2024)
- [Mitigating Catastrophic Forgetting for Few-Shot Spoken Word Classification Through Meta-Learning](https://mlsanity.com/paper/2305.13080/) (2023)
- [Learning Separable Hidden Unit Contributions for Speaker-Adaptive Lip-Reading](https://mlsanity.com/paper/2310.05058/) (2023)
- [Meta Input: How to Leverage Off-the-Shelf Deep Neural Networks](https://mlsanity.com/paper/2210.13186/) (2022)

## Abstract (arXiv)

Speaker adaptive training (SAT) of neural network acoustic models learns models in a way that makes them more suitable for adaptation to test conditions. Conventionally, model-based speaker adaptive training is performed by having a set of speaker dependent parameters that are jointly optimised with speaker independent parameters in order to remove speaker variation. However, this does not scale well if all neural network weights are to be adapted to the speaker. In this paper we formulate speaker adaptive training as a meta-learning task, in which an adaptation process using gradient descent is encoded directly into the training of the model. We compare our approach with test-only adaptation of a standard baseline model and a SAT-LHUC model with a learned speaker adaptation schedule and demonstrate that the meta-learning approach achieves comparable results.