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

Many self-supervised speech models, varying in their pre-training objective, input modality,
and pre-training data, have been proposed in the last few years. Despite impressive …

We present a method for visually-grounded spoken term discovery. After training either a
HuBERT or wav2vec2. 0 model to associate spoken captions with natural images, we show …

Data-driven speech processing models usually perform well with a large amount of text
supervision, but collecting transcribed speech data is costly. Therefore, we propose Speech …

This survey provides an overview of the evolution of visually grounded models of spoken
language over the last 20 years. Such models are inspired by the observation that when …

In this paper, we describe our submissions to the ZeroSpeech 2021 Challenge and
SUPERB benchmark. Our submissions are based on the recently proposed FaST-VGS …

We propose a novel framework for target speech extraction based on semantic information,
called ConceptBeam. Target speech extraction means extracting the speech of a target …

Pathological captioning of Whole Slide Images (WSIs), though is essential in computer-
aided pathological diagnosis, has rarely been studied due to the limitations in datasets and …

Recent computational models of the acquisition of spoken language via grounding in
perception exploit associations between spoken and visual modalities and learn to …

This work investigates the use of large-scale, English-only pre-trained models (CLIP and
HuBERT) for multilingual image-speech retrieval. For non-English image-speech retrieval …