Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

Similar to humans' cognitive ability to generalize knowledge and skills, self-supervised
learning (SSL) targets discovering general representations from large-scale data. This …

We explore unsupervised pre-training for speech recognition by learning representations of
raw audio. wav2vec is trained on large amounts of unlabeled audio data and the resulting …

Despite rapid progress in the recent past, current speech recognition systems still require
labeled training data which limits this technology to a small fraction of the languages spoken …

We consider the task of unsupervised extraction of meaningful latent representations of
speech by applying autoencoding neural networks to speech waveforms. The goal is to …

Although multi-view learning has made significant progress over the past few decades, it is
still challenging due to the difficulty in modeling complex correlations among different views …

We introduce a new collection of spoken English audio suitable for training speech
recognition systems under limited or no supervision. It is derived from open-source audio …

We describe a method to jointly pre-train speech and text in an encoder-decoder modeling
framework for speech translation and recognition. The proposed method incorporates four …

Unsupervised speech recognition has shown great potential to make Automatic Speech
Recognition (ASR) systems accessible to every language. However, existing methods still …

In recent decades, wildfires have caused tremendous property losses, fatalities, and
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …