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

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