3 citations · 9 across the 10 of their papers we have counts for
10 papers
Acoustic Cue Alignment in Audio Language Models for Speech Emotion Recognition
Iosif Tsangko, Andreas Triantafyllopoulos, Björn W. Schuller
Instruction-following audio language models (ALMs) can be augmented with explicit acoustic cues, yet it remains unclear whether such cues are used in a grounded way when the raw au…
A Pilot Study on Curator-Guided Multilingual Art Description for Blind and Low-Vision Audiences with Small Vision-Language Models
Iosif Tsangko, Andreas Triantafyllopoulos, George Margetis +2
Blind and low-vision (BLV) audiences remain underserved by visual art descriptions, particularly across languages and in museum settings where privacy and intellectual-property con…
Reading Smiles: Proxy Bias in Foundation Models for Facial Emotion Recognition
Iosif Tsangko, Andreas Triantafyllopoulos, Adem Abdelmoula +2
Foundation Models (FMs) are rapidly transforming Affective Computing (AC), with Vision Language Models (VLMs) now capable of recognising emotions in zero shot settings. This paper…
MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge
Xin Jing, Jiadong Wang, Iosif Tsangko +2
Although speech emotion recognition (SER) has advanced significantly with deep learning, annotation remains a major hurdle. Human annotation is not only costly but also subject to…
DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids
Iosif Tsangko, Andreas Triantafyllopoulos, Michael Müller +2
The DeepFilterNet (DFN) architecture was recently proposed as a deep learning model suited for hearing aid devices. Despite its competitive performance on numerous benchmarks, it s…
Large language models for mental health
Andreas Triantafyllopoulos, Yannik Terhorst, Iosif Tsangko +10
Digital technologies have long been explored as a complement to standard procedure in mental health research and practice, ranging from the management of electronic health records…
Affective Computing Has Changed: The Foundation Model Disruption
Björn Schuller, Adria Mallol-Ragolta, Alejandro Peña Almansa +5
The dawn of Foundation Models has on the one hand revolutionised a wide range of research problems, and, on the other hand, democratised the access and use of AI-based tools by the…
Abusive Speech Detection in Indic Languages Using Acoustic Features
Anika A. Spiesberger, Andreas Triantafyllopoulos, Iosif Tsangko +1
Abusive content in online social networks is a well-known problem that can cause serious psychological harm and incite hatred. The ability to upload audio data increases the import…
Computer Audition: From Task-Specific Machine Learning to Foundation Models
Andreas Triantafyllopoulos, Iosif Tsangko, Alexander Gebhard +3
Foundation models (FMs) are increasingly spearheading recent advances on a variety of tasks that fall under the purview of computer audition -- the use of machines to understand so…
Bringing the Discussion of Minima Sharpness to the Audio Domain: a Filter-Normalised Evaluation for Acoustic Scene Classification
Manuel Milling, Andreas Triantafyllopoulos, Iosif Tsangko +2
The correlation between the sharpness of loss minima and generalisation in the context of deep neural networks has been subject to discussion for a long time. Whilst mostly investi…