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

• URL: http://arxiv.org/abs/2305.18283v1
• Date: Mon, 29 May 2023 17:53:35 GMT
• Title: CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice
• Authors: Juan Zuluaga-Gomez and Sara Ahmed and Danielius Visockas and Cem Subakan
• Abstract summary: We introduce a 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) We establish new state-of-the-art for English accent classification with as high as 95% accuracy.
• Score: 1.559929646151698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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)

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