Deep learning methods haverevolutionized speech recognition, image recognition, and
natural language processing since 2010. Each of these tasks involves a single modality in …

Speech enhancement and speech separation are two related tasks, whose purpose is to
extract either one or more target speech signals, respectively, from a mixture of sounds …

Language-queried audio source separation (LASS) is a new paradigm for computational
auditory scene analysis (CASA). LASS aims to separate a target sound from an audio …

Speaker extraction aims to extract the target speech signal from a multi-talker environment
given a target speaker's reference speech. We recently proposed a time-domain solution …

Target speech separation refers to extracting a target speaker's voice from an overlapped
audio of simultaneous talkers. Previously the use of visual modality for target speech …

Humans use multimodal sensory information to understand the physical properties of their
environment. Intelligent decision-making systems such as the ones used in robotic …

Automatic recognition of overlapped speech remains a highly challenging task to date.
Motivated by the bimodal nature of human speech perception, this paper investigates the …

A speaker extraction algorithm seeks to extract the target speaker's speech from a multi-
talker speech mixture. The prior studies focus mostly on speaker extraction from a highly …

This paper describes a system that generates speaker-annotated transcripts of meetings by
using a microphone array and a 360-degree camera. The hallmark of the system is its ability …

Humans possess the remarkable ability to selectively attend to a single speaker amidst
competing voices and background noise, known as selective auditory attention. Recent …