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.

This repository provides all the necessary tools to perform accent identification from speech recordings with SpeechBrain. The system uses a model pretrained on the CommonAccent dataset in Italian (5 accents). This system is based on the CommonLanguage Recipe located here: https://github.com/speechbrain/speechbrain/tree/develop/recipes/CommonLanguage

The provided system can recognize the following 5 accents from short speech recordings in Italian (IT):

- VENETO
- EMILIANO
- MERIDIONALE
- TENDENTE AL SICULO MA NON MARCATO
- BASILICATA TRENTINO

Github repository link: https://github.com/JuanPZuluaga/accent-recog-slt2022

NOTE: due to incompatibility with the model and the current SpeechBrain interfaces, we cannot offer the Inference API. Please, follow the steps in "Perform Accent Identification from Speech Recordings" to use this Italian Accent ID model.

For a better experience, we encourage you to learn more about SpeechBrain.

This system is composed of a fine-tuned XLSR model coupled with statistical pooling. A classifier, trained with NLL Loss, is applied on top of that.

The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling classify_file if needed. Make sure your input tensor is compliant with the expected sampling rate if you use encode_batch and classify_batch.

First of all, please install SpeechBrain with the following command:

Please notice that we encourage you to read our tutorials and learn more about SpeechBrain.

To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.

The model was trained with SpeechBrain.

To train it from scratch follow these steps:

You can find our training results (models, logs, etc) in this repository's Files and versions page.

The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

If you find useful this work, please cite our work as:

@article{zuluaga2023commonaccent,
  title={CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice},
  author={Zuluaga-Gomez, Juan and Ahmed, Sara and Visockas, Danielius and Subakan, Cem},
  journal={Interspeech 2023},
  url={https://arxiv.org/abs/2305.18283},
  year={2023}
}

Please, cite SpeechBrain if you use it for your research or business.