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Bethan Thomas

2 accepted papers

2022

An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning

ICASSP 2022accepted

We present a method for transferring pre-trained self-supervised (SSL) speech representations to multiple languages. There is an abundance of unannotated speech, so creating self-supervised representations from raw audio and fine-tuning on small annotated datasets is a promising direction to build s…

Cited by 0SourceScholar
2022

Efficient Adapter Transfer of Self-Supervised Speech Models for Automatic Speech Recognition

ICASSP 2022accepted

Self-supervised learning (SSL) is a powerful tool that allows learning of underlying representations from unlabeled data. Transformer based models such as wav2vec 2.0 and HuBERT are leading the field in the speech domain. Generally these models are fine-tuned on a small amount of labeled data for a…

Cited by 0SourceScholar