# Improving Low-Resource Dialect Classification Using Retrieval-based Voice Conversion

arXiv: https://arxiv.org/abs/2507.03641 · HTML: https://mlsanity.com/paper/2507.03641/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 4 Jul 2025.

## Builds on (most cited first, 12 of 12)

- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [NeMo: a toolkit for building AI applications using Neural Modules](https://mlsanity.com/paper/1909.09577/) (2019)
- [An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2008.03648/) (2020)
- [Natural Language Processing for Dialects of a Language: A Survey](https://mlsanity.com/paper/2401.05632/) (2024)
- [SpecMix : A Mixed Sample Data Augmentation method for Training withTime-Frequency Domain Features](https://mlsanity.com/paper/2108.03020/) (2021)
- [TRILLsson: Distilled Universal Paralinguistic Speech Representations](https://mlsanity.com/paper/2203.00236/) (2022)
- [Voice Conversion Can Improve ASR in Very Low-Resource Settings](https://mlsanity.com/paper/2111.02674/) (2021)
- [Voice Conversion Augmentation for Speaker Recognition on Defective Datasets](https://mlsanity.com/paper/2404.00863/) (2024)
- [Custom Data Augmentation for low resource ASR using Bark and Retrieval-Based Voice Conversion](https://mlsanity.com/paper/2311.14836/) (2023)
- [Measuring the Effectiveness of Voice Conversion on Speaker Identification and Automatic Speech Recognition Systems](https://mlsanity.com/paper/1905.12531/) (2019)

## Built on by (most cited first, 1 of 1)

- [Can LLM Agents Identify Spoken Dialects like a Linguist?](https://mlsanity.com/paper/2603.29541/) (2026)

## Abstract (arXiv)

Deep learning models for dialect identification are often limited by the scarcity of dialectal data. To address this challenge, we propose to use Retrieval-based Voice Conversion (RVC) as an effective data augmentation method for a low-resource German dialect classification task. By converting audio samples to a uniform target speaker, RVC minimizes speaker-related variability, enabling models to focus on dialect-specific linguistic and phonetic features. Our experiments demonstrate that RVC enhances classification performance when utilized as a standalone augmentation method. Furthermore, combining RVC with other augmentation methods such as frequency masking and segment removal leads to additional performance gains, highlighting its potential for improving dialect classification in low-resource scenarios.