# AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition

arXiv: https://arxiv.org/abs/2005.07973 · HTML: https://mlsanity.com/paper/2005.07973/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2020.

## Built on by (most cited first, 19 of 19)

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- [DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding](https://mlsanity.com/paper/2601.23161/) (2026)
- [CAAD: Contrastive Audio-Aware Distillation for Efficient Speech Language Models](https://mlsanity.com/paper/2606.23052/) (2026)
- [Unsupervised Out-of-Distribution Dialect Detection with Mahalanobis Distance](https://mlsanity.com/paper/2308.04886/) (2023)
- [Task-Lens: Cross-Task Utility Based Speech Dataset Profiling for Low-Resource Indian Languages](https://mlsanity.com/paper/2602.23388/) (2026)
- [Saar-Voice: A Multi-Speaker Saarbr"ucken Dialect Speech Corpus](https://mlsanity.com/paper/2604.11803/) (2026)
- [Revisiting Accent Classification with DistilHuBERT: Speaker-Disjoint Evaluation and Cross-Corpus Generalization](https://mlsanity.com/paper/2610.13440/) (2026)

## Abstract (arXiv)

Modern Automatic Speech Recognition (ASR) technology has evolved to identify the speech spoken by native speakers of a language very well. However, identification of the speech spoken by non-native speakers continues to be a major challenge for it. In this work, we first spell out the key requirements for creating a well-curated database of speech samples in non-native accents for training and testing robust ASR systems. We then introduce AccentDB, one such database that contains samples of 4 Indian-English accents collected by us, and a compilation of samples from 4 native-English, and a metropolitan Indian-English accent. We also present an analysis on separability of the collected accent data. Further, we present several accent classification models and evaluate them thoroughly against human-labelled accent classes. We test the generalization of our classifier models in a variety of setups of seen and unseen data. Finally, we introduce the task of accent neutralization of non-native accents to native accents using autoencoder models with task-specific architectures. Thus, our work aims to aid ASR systems at every stage of development with a database for training, classification models for feature augmentation, and neutralization systems for acoustic transformations of non-native accents of English.