# Accented Speech Recognition: A Survey

arXiv: https://arxiv.org/abs/2104.10747 · HTML: https://mlsanity.com/paper/2104.10747/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 13 of 13)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Progress & Compress: A scalable framework for continual learning](https://mlsanity.com/paper/1805.06370/) (2018)
- [Achieving Human Parity in Conversational Speech Recognition](https://mlsanity.com/paper/1610.05256/) (2016)
- [Personalizing ASR for Dysarthric and Accented Speech with Limited Data](https://mlsanity.com/paper/1907.13511/) (2019)
- [Domain Adversarial Training for Accented Speech Recognition](https://mlsanity.com/paper/1806.02786/) (2018)
- [Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model](https://mlsanity.com/paper/1712.01541/) (2017)
- [Joint Modeling of Accents and Acoustics for Multi-Accent Speech Recognition](https://mlsanity.com/paper/1802.02656/) (2018)
- [AIPNet: Generative Adversarial Pre-training of Accent-invariant Networks for End-to-end Speech Recognition](https://mlsanity.com/paper/1911.11935/) (2019)
- [Multi-Accent Adaptation based on Gate Mechanism](https://mlsanity.com/paper/2011.02774/) (2020)
- [Black-box Adaptation of ASR for Accented Speech](https://mlsanity.com/paper/2006.13519/) (2020)
- [Leveraging native language information for improved accented speech recognition](https://mlsanity.com/paper/1904.09038/) (2019)
- [Domain Expansion in DNN-based Acoustic Models for Robust Speech Recognition](https://mlsanity.com/paper/1910.00565/) (2019)

## Built on by (most cited first, 12 of 12)

- [CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice](https://mlsanity.com/paper/2305.18283/) (2023)
- [Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond](https://mlsanity.com/paper/2502.03945/) (2025)
- [AfriNames: Most ASR models "butcher" African Names](https://mlsanity.com/paper/2306.00253/) (2023)
- [Global Performance Disparities Between English-Language Accents in Automatic Speech Recognition](https://mlsanity.com/paper/2208.01157/) (2022)
- [A Deep Dive into the Disparity of Word Error Rates Across Thousands of NPTEL MOOC Videos](https://mlsanity.com/paper/2307.10587/) (2023)
- [Some voices are too common: Building fair speech recognition systems using the Common Voice dataset](https://mlsanity.com/paper/2306.03773/) (2023)
- [AccentFold: A Journey through African Accents for Zero-Shot ASR Adaptation to Target Accents](https://mlsanity.com/paper/2402.01152/) (2024)
- [Measuring the Impact of Individual Domain Factors in Self-Supervised Pre-Training](https://mlsanity.com/paper/2203.00648/) (2022)
- [Effects of Speaker Count, Duration, and Accent Diversity on Zero-Shot Accent Robustness in Low-Resource ASR](https://mlsanity.com/paper/2506.04364/) (2025)
- [Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech](https://mlsanity.com/paper/2112.13758/) (2021)
- [Nollywood: Let's Go to the Movies!](https://mlsanity.com/paper/2407.02631/) (2024)
- [On-Policy Self-Distillation for Multi-Dialect ASR: Mastering Dialects, Retaining Mandarin](https://mlsanity.com/paper/2608.11898/) (2026)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) systems generalize poorly on accented speech. The phonetic and linguistic variability of accents present hard challenges for ASR systems today in both data collection and modeling strategies. The resulting bias in ASR performance across accents comes at a cost to both users and providers of ASR. We present a survey of current promising approaches to accented speech recognition and highlight the key challenges in the space. Approaches mostly focus on single model generalization and accent feature engineering. Among the challenges, lack of a standard benchmark makes research and comparison especially difficult.