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

The taking of turns is a fundamental aspect of dialogue. Since it is difficult to speak and listen
at the same time, the participants need to coordinate who is currently speaking and when …

Speech brain–computer interfaces (BCIs) have the potential to restore rapid communication
to people with paralysis by decoding neural activity evoked by attempted speech into text, or …

Large-scale generative models such as GPT and DALL-E have revolutionized the research
community. These models not only generate high fidelity outputs, but are also generalists …

Conformer has proven to be effective in many speech processing tasks. It combines the
benefits of extracting local dependencies using convolutions and global dependencies …

Abstract Machine learning research has long focused on models rather than datasets, and
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …

Sequence-to-sequence models have been widely used in end-to-end speech processing,
for example, automatic speech recognition (ASR), speech translation (ST), and text-to …

The objective of this work is speaker recognition under noisy and unconstrained conditions.
We make two key contributions. First, we introduce a very large-scale audio-visual dataset …

We present SpecAugment, a simple data augmentation method for speech recognition.
SpecAugment is applied directly to the feature inputs of a neural network (ie, filter bank …

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 …