# Unsupervised Domain Adaptation in Speech Recognition using Phonetic Features

arXiv: https://arxiv.org/abs/2108.02850 · HTML: https://mlsanity.com/paper/2108.02850/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Large-scale Multi-label Text Classification - Revisiting Neural Networks](https://mlsanity.com/paper/1312.5419/) (2013)
- [A Multi-Discriminator CycleGAN for Unsupervised Non-Parallel Speech Domain Adaptation](https://mlsanity.com/paper/1804.00522/) (2018)
- [Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation](https://mlsanity.com/paper/1807.00374/) (2018)

## Abstract (arXiv)

Automatic speech recognition is a difficult problem in pattern recognition because several sources of variability exist in the speech input like the channel variations, the input might be clean or noisy, the speakers may have different accent and variations in the gender, etc. As a result, domain adaptation is important in speech recognition where we train the model for a particular source domain and test it on a different target domain. In this paper, we propose a technique to perform unsupervised gender-based domain adaptation in speech recognition using phonetic features. The experiments are performed on the TIMIT dataset and there is a considerable decrease in the phoneme error rate using the proposed approach.