Bethan Thomas

Bethan Thomas, Samuel Kessler, Salah Karout

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 downstream task such as Automatic Speech Recognition (ASR). This involves re-training the majority of the model for each task. Adapters are small lightweight modules which are commonly used in Natural Language Processing (NLP) to adapt pre-trained models to new tasks. In this paper we propose applying adapters to wav2vec 2.0 to reduce the number of parameters required for downstream ASR tasks, and increase scalability of the model to multiple tasks or languages. Using adapters we can perform ASR while training fewer than 10% of parameters per task compared to full fine-tuning with little degradation of performance. Ablations show that applying adapters into just the top few layers of the pre-trained network gives similar performance to full transfer, supporting the theory that higher pre-trained layers encode more phonemic information, and further optimizing efficiency.

Samuel Kessler, Bethan Thomas, Salah Karout

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 speech recognition systems. SSL models generally perform SSL on raw audio in a pre-training phase and then fine-tune on a small fraction of annotated data. Such models have produced state of the art results for ASR. However, these models are very expensive to pre-train. We use an existing wav2vec 2.0 model and tackle the problem of learning new language representations while utilizing existing model knowledge. Crucially we do so without catastrophic forgetting of the existing language representation. We use adapter modules to speed up pre-training a new language task. Our model can decrease pre-training times by 32% when learning a new language task, and learn this new audio-language representation without forgetting previous language representation. We evaluate by applying these language representations to automatic speech recognition.