Imagine standing on a street corner in the city. With your eyes closed you can hear and
recognize a succession of sounds: cars passing by, people speaking, their footsteps when …

Deep learning (DL), as one of the most active machine learning research fields, has
achieved great success in numerous scientific and technological disciplines, including …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

Speech processing Universal PERformance Benchmark (SUPERB) is a leaderboard to
benchmark the performance of Self-Supervised Learning (SSL) models on various speech …

We describe our development of CSS10, a collection of single speaker speech datasets for
ten languages. It is composed of short audio clips from LibriVox audiobooks and their …

The growth in online child exploitation material is a significant challenge for European Law
Enforcement Agencies (LEAs). One of the most important sources of such online information …

The goal of this work is to train strong models for visual speech recognition without requiring
human annotated ground truth data. We achieve this by distilling from an Automatic Speech …

Collecting large amounts of data to train end-to-end Speech Translation (ST) models is more
difficult compared to the ASR and MT tasks. Previous studies have proposed the use of …

High quality Automatic Speech Recognition (ASR) is a prerequisite for speech-based
applications and research. While state-of-the-art ASR software is freely available, the …

Our ability to comprehend speech remains, to date, unrivaled by deep learning models. This
feat could result from the brain's ability to fine-tune generic sound representations for speech …