# Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-supervised Learning

arXiv: https://arxiv.org/abs/2109.07349 · HTML: https://mlsanity.com/paper/2109.07349/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2021.

## Built on by (most cited first, 13 of 13)

- [What Can an Accent Identifier Learn? Probing Phonetic and Prosodic Information in a Wav2vec2-based Accent Identification Model](https://mlsanity.com/paper/2306.06524/) (2023)
- [Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model](https://mlsanity.com/paper/2112.07254/) (2021)
- [Improving CTC-based speech recognition via knowledge transferring from pre-trained language models](https://mlsanity.com/paper/2203.03582/) (2022)
- [Improving the Training Recipe for a Robust Conformer-based Hybrid Model](https://mlsanity.com/paper/2206.12955/) (2022)
- [Wav2vec-S: Semi-Supervised Pre-Training for Low-Resource ASR](https://mlsanity.com/paper/2110.04484/) (2021)
- [Accented Speech Recognition With Accent-specific Codebooks](https://mlsanity.com/paper/2310.15970/) (2023)
- [Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis](https://mlsanity.com/paper/2407.04047/) (2024)
- [Improving Children's Speech Recognition by Fine-tuning Self-supervised Adult Speech Representations](https://mlsanity.com/paper/2211.07769/) (2022)
- [Multi-pass Training and Cross-information Fusion for Low-resource End-to-end Accented Speech Recognition](https://mlsanity.com/paper/2306.11309/) (2023)
- [Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications](https://mlsanity.com/paper/2502.20311/) (2025)
- [Improving Self-supervised Pre-training using Accent-Specific Codebooks](https://mlsanity.com/paper/2407.03734/) (2024)
- [Probing for Phonology in Self-Supervised Speech Representations: A Case Study on Accent Perception](https://mlsanity.com/paper/2506.17542/) (2025)
- [Revisiting Accent Classification with DistilHuBERT: Speaker-Disjoint Evaluation and Cross-Corpus Generalization](https://mlsanity.com/paper/2610.13440/) (2026)

## Abstract (arXiv)

Recently, self-supervised pre-training has gained success in automatic speech recognition (ASR). However, considering the difference between speech accents in real scenarios, how to identify accents and use accent features to improve ASR is still challenging. In this paper, we employ the self-supervised pre-training method for both accent identification and accented speech recognition tasks. For the former task, a standard deviation constraint loss (SDC-loss) based end-to-end (E2E) architecture is proposed to identify accents under the same language. As for accented speech recognition task, we design an accent-dependent ASR system, which can utilize additional accent input features. Furthermore, we propose a frame-level accent feature, which is extracted based on the proposed accent identification model and can be dynamically adjusted. We pre-train our models using 960 hours unlabeled LibriSpeech dataset and fine-tune them on AESRC2020 speech dataset. The experimental results show that our proposed accent-dependent ASR system is significantly ahead of the AESRC2020 baseline and achieves 6.5% relative word error rate (WER) reduction compared with our accent-independent ASR system.