In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

Recently, speech-text pre-training methods have shown remarkable success in many
speech and natural language processing tasks. However, most previous pre-trained models …

We address the problem of detecting who spoke when in child-inclusive spoken interactions
ie, automatic child-adult speaker classification. Interactions involving children are richly …

Audio-Language Models (ALMs), which are trained on audio-text data, focus on the
processing, understanding, and reasoning of sounds. Unlike traditional supervised learning …

Contrastive learning has gained significant attention as a method for self-supervised
learning. The contrastive loss function ensures that embeddings of positive sample pairs …

Recently, speech-text pre-training methods have shown remarkable success in many
speech and natural language processing tasks. However, most previous pre-trained models …

End-to-end spoken language understanding requires speech data annotated with semantic
information and may suffer from the shortage of annotated data. Recent progresses leverage …

Abstract Machine Learning (ML) research is going through arguably one of the most volatile
periods in its history. While this is incredibly exciting has led to amazing discoveries, it has …

The Greek Language Multimodal Lip Reading with Integrated Sign Language Accessibility
(GLaM-Sign)[1] is a groundbreaking resource in accessibility and multimodal AI, designed to …

Abstract Information overload is a major problem affecting today's society. The continuous
stream of information makes it impossible for individuals to process and understand the vast …