# Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data

arXiv: https://arxiv.org/abs/2205.08014 · HTML: https://mlsanity.com/paper/2205.08014/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2022.

## Built on by (most cited first, 15 of 15)

- [Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation](https://mlsanity.com/paper/2305.10951/) (2023)
- [Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond](https://mlsanity.com/paper/2502.03945/) (2025)
- [A Multi-Dialectal Dataset for German Dialect ASR and Dialect-to-Standard Speech Translation](https://mlsanity.com/paper/2506.02894/) (2025)
- [Global Performance Disparities Between English-Language Accents in Automatic Speech Recognition](https://mlsanity.com/paper/2208.01157/) (2022)
- [Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning](https://mlsanity.com/paper/2208.03067/) (2022)
- [Accented Speech Recognition With Accent-specific Codebooks](https://mlsanity.com/paper/2310.15970/) (2023)
- [Self-supervised Speech Representations Still Struggle with African American Vernacular English](https://mlsanity.com/paper/2408.14262/) (2024)
- [Improving Speech Recognition for African American English With Audio Classification](https://mlsanity.com/paper/2309.09996/) (2023)
- [AccentFold: A Journey through African Accents for Zero-Shot ASR Adaptation to Target Accents](https://mlsanity.com/paper/2402.01152/) (2024)
- [Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications](https://mlsanity.com/paper/2502.20311/) (2025)
- [Accented Text-to-Speech Synthesis with a Conditional Variational Autoencoder](https://mlsanity.com/paper/2211.03316/) (2022)
- [Incorporating L2 Phonemes Using Articulatory Features for Robust Speech Recognition](https://mlsanity.com/paper/2306.02534/) (2023)
- [Goodness of Pronunciation Pipelines for OOV Problem](https://mlsanity.com/paper/2209.03787/) (2022)
- [Speech Corpora Divergence Based Unsupervised Data Selection for ASR](https://mlsanity.com/paper/2302.13222/) (2023)
- [Framework for Curating Speech Datasets and Evaluating ASR Systems: A Case Study for Polish](https://mlsanity.com/paper/2408.00005/) (2024)

## Abstract (arXiv)

Building inclusive speech recognition systems is a crucial step towards developing technologies that speakers of all language varieties can use. Therefore, ASR systems must work for everybody independently of the way they speak. To accomplish this goal, there should be available data sets representing language varieties, and also an understanding of model configuration that is the most helpful in achieving robust understanding of all types of speech. However, there are not enough data sets for accented speech, and for the ones that are already available, more training approaches need to be explored to improve the quality of accented speech recognition. In this paper, we discuss recent progress towards developing more inclusive ASR systems, namely, the importance of building new data sets representing linguistic diversity, and exploring novel training approaches to improve performance for all users. We address recent directions within benchmarking ASR systems for accented speech, measure the effects of wav2vec 2.0 pre-training on accented speech recognition, and highlight corpora relevant for diverse ASR evaluations.