Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

Self-supervised representation learning (SSRL) methods aim to provide powerful, deep
feature learning without the requirement of large annotated data sets, thus alleviating the …

Expanding the language coverage of speech technology has the potential to improve
access to information for many more people. However, current speech technology is …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

Transfer learning has recently become the dominant paradigm of machine learning. Pre-
trained models fine-tuned for downstream tasks achieve better performance with fewer …

This paper presents XLSR which learns cross-lingual speech representations by pretraining
a single model from the raw waveform of speech in multiple languages. We build on …

Wav2vec 2.0 is a recently proposed self-supervised framework for speech representation
learning. It follows a two-stage training process of pre-training and fine-tuning, and performs …

We present mSLAM, a multilingual Speech and LAnguage Model that learns cross-lingual
cross-modal representations of speech and text by pre-training jointly on large amounts of …

This work dedicates to continuous sign language recognition (CSLR), which is a weakly
supervised task dealing with the recognition of continuous signs from videos, without any …

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 …