# CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice

arXiv: https://arxiv.org/abs/2305.18283 · HTML: https://mlsanity.com/paper/2305.18283/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 29 May 2023.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Datasets: A Community Library for Natural Language Processing](https://mlsanity.com/paper/2109.02846/) (2021)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [Utterance-level Aggregation For Speaker Recognition In The Wild](https://mlsanity.com/paper/1902.10107/) (2019)
- [The Accented English Speech Recognition Challenge 2020: Open Datasets, Tracks, Baselines, Results and Methods](https://mlsanity.com/paper/2102.10233/) (2021)
- [Accented Speech Recognition: A Survey](https://mlsanity.com/paper/2104.10747/) (2021)
- [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)
- [Exploiting Spectral Augmentation for Code-Switched Spoken Language Identification](https://mlsanity.com/paper/2010.07130/) (2020)
- [AISPEECH-SJTU accent identification system for the Accented English Speech Recognition Challenge](https://mlsanity.com/paper/2102.09828/) (2021)
- [English Accent Accuracy Analysis in a State-of-the-Art Automatic Speech Recognition System](https://mlsanity.com/paper/2105.05041/) (2021)
- [Deep Discriminative Feature Learning for Accent Recognition](https://mlsanity.com/paper/2011.12461/) (2020)

## Built on by (most cited first, 20 of 20)

- [Speech-Copilot: Leveraging Large Language Models for Speech Processing via Task Decomposition, Modularization, and Program Generation](https://mlsanity.com/paper/2407.09886/) (2024)
- [Convert and Speak: Zero-shot Accent Conversion with Minimum Supervision](https://mlsanity.com/paper/2408.10096/) (2024)
- [CapSpeech: Enabling Downstream Applications in Style-Captioned Text-to-Speech](https://mlsanity.com/paper/2506.02863/) (2025)
- [AccentBox: Towards High-Fidelity Zero-Shot Accent Generation](https://mlsanity.com/paper/2409.09098/) (2024)
- [Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs](https://mlsanity.com/paper/2512.16378/) (2025)
- [MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks](https://mlsanity.com/paper/2507.23511/) (2025)
- [Voxlect: A Speech Foundation Model Benchmark for Modeling Dialects and Regional Languages Around the Globe](https://mlsanity.com/paper/2508.01691/) (2025)
- [Pairwise Evaluation of Accent Similarity in Speech Synthesis](https://mlsanity.com/paper/2505.14410/) (2025)
- [Big model only for hard audios: Sample dependent Whisper model selection for efficient inferences](https://mlsanity.com/paper/2309.12712/) (2023)
- [Accent-Invariant Automatic Speech Recognition via Saliency-Driven Spectrogram Masking](https://mlsanity.com/paper/2510.09528/) (2025)
- [Rethinking Discrete Speech Representation Tokens for Accent Generation](https://mlsanity.com/paper/2601.19786/) (2026)
- [MaskVCT: Masked Voice Codec Transformer for Zero-Shot Voice Conversion With Increased Controllability via Multiple Guidances](https://mlsanity.com/paper/2509.17143/) (2025)
- [Probing for Phonology in Self-Supervised Speech Representations: A Case Study on Accent Perception](https://mlsanity.com/paper/2506.17542/) (2025)
- [The Prosody of Emojis](https://mlsanity.com/paper/2508.00537/) (2025)
- [StyleStream: Real-Time Zero-Shot Voice Style Conversion](https://mlsanity.com/paper/2602.20113/) (2026)
- [Voice "Cloning" is Style Transfer](https://mlsanity.com/paper/2605.16578/) (2026)
- [TokAN: Accent Normalization Using Self-Supervised Speech Tokens](https://mlsanity.com/paper/2607.03928/) (2026)
- [Flexible and Interpretable Accent Distance Measurements](https://mlsanity.com/paper/2609.11458/) (2026)
- [FuseAlign: Forced Alignment in the Wild](https://mlsanity.com/paper/2609.33650/) (2026)
- [AccentCL: Robust Accent Classification with Incremental Expansion](https://mlsanity.com/paper/2610.07426/) (2026)

## Abstract (arXiv)

Despite the recent advancements in Automatic Speech Recognition (ASR), the recognition of accented speech still remains a dominant problem. In order to create more inclusive ASR systems, research has shown that the integration of accent information, as part of a larger ASR framework, can lead to the mitigation of accented speech errors. We address multilingual accent classification through the ECAPA-TDNN and Wav2Vec 2.0/XLSR architectures which have been proven to perform well on a variety of speech-related downstream tasks. We introduce a simple-to-follow recipe aligned to the SpeechBrain toolkit for accent classification based on Common Voice 7.0 (English) and Common Voice 11.0 (Italian, German, and Spanish). Furthermore, we establish new state-of-the-art for English accent classification with as high as 95% accuracy. We also study the internal categorization of the Wav2Vev 2.0 embeddings through t-SNE, noting that there is a level of clustering based on phonological similarity. (Our recipe is open-source in the SpeechBrain toolkit, see: https://github.com/speechbrain/speechbrain/tree/develop/recipes)