CONF Juan_INTERSPEECH_2023/IDIAP CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice Zuluaga-Gomez, Juan Sara, Ahmed Danielius, Visockas Cem, Subakan automatic accent classification Common Voice dataset ECAPA-TDNN SpeechBrain wav2vec 2.0 EXTERNAL http://publications.idiap.ch/attachments/papers/2023/Juan_INTERSPEECH_2023.pdf PUBLIC Proc. Interspeech 2023 2023 https://arxiv.org/abs/2305.18283 URL 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.