In this study, we present recent developments on ESPnet: End-to-End Speech Processing
toolkit, which mainly involves a recently proposed architecture called Conformer …

Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep
neural networks (DNNs) to execute complex inference tasks such as image classification …

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

We introduce a language modeling approach for text to speech synthesis (TTS). Specifically,
we train a neural codec language model (called Vall-E) using discrete codes derived from …

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 …

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …

This paper studies a simple extension of image-based Masked Autoencoders (MAE) to self-
supervised representation learning from audio spectrograms. Following the Transformer …

SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the
research and development of neural speech processing technologies by being simple …

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 …