Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

Self-supervised learning of speech representations has been a very active research area
but most work is focused on a single domain such as read audio books for which there exist …

Source-free domain adaptation (SFDA) is compelling because it allows adapting an off-the-
shelf model to a new domain using only unlabelled data. In this work, we apply existing …

End-to-end automatic speech recognition (ASR) has become a popular alternative to
traditional module-based systems, simplifying the model-building process with a single deep …

We propose a simple and effective cross-lingual transfer learning method to adapt
monolingual wav2vec-2.0 models for Automatic Speech Recognition (ASR) in resource …

Pseudo-labeling (PL) has been shown to be effective in semi-supervised automatic speech
recognition (ASR), where a base model is self-trained with pseudo-labels generated from …

Semi-supervised learning has demonstrated promising results in automatic speech
recognition (ASR) by self-training using a seed ASR model with pseudo-labels generated for …

When labeled data is insufficient, pseudo-labeling based semi-supervised learning can
significantly improve the performance of automatic speech recognition. However, pseudo …

Abstract Representation learning or pre-training has shown promising performance for low-
resource speech recognition which suffers from the data shortage. Recently, self-supervised …