CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice
| Type of publication: | Conference paper |
| Citation: | Juan_INTERSPEECH_2023 |
| Publication status: | Accepted |
| Booktitle: | Proc. Interspeech 2023 |
| Year: | 2023 |
| URL: | https://arxiv.org/abs/2305.182... |
| 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. |
| Keywords: | automatic accent classification, Common Voice dataset, ECAPA-TDNN, SpeechBrain, wav2vec 2.0 |
| Projects: |
Idiap EC H2020-ROXANNE |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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