Elisabeth André

Deep Learning in Paralinguistic Recognition Tasks: Are Hand-crafted Features Still Relevant?

2018

In the past, the performance of machine learning algorithms depended heavily on the representation of the data.Well-designed features therefore played a key role in speech and paralinguistic recognition tasks.Consequently, engineers have put a great deal …

A Generic Human–Machine Annotation Framework Based on Dynamic Cooperative Learning

2019 · IEEE Transactions on Cybernetics

The task of obtaining meaningful annotations is a tedious work, incurring considerable costs and time consumption. Dynamic active learning and cooperative learning are recently proposed approaches to reduce human effort of annotating data with subjective …

“Let me explain!”: exploring the potential of virtual agents in explainable AI interaction design

2020 · Journal on Multimodal User Interfaces

Abstract While the research area of artificial intelligence benefited from increasingly sophisticated machine learning techniques in recent years, the resulting systems suffer from a loss of transparency and comprehensibility, especially for end-users. In this paper, …

Unraveling ML Models of Emotion With NOVA: Multi-Level Explainable AI for Non-Experts

2020 · IEEE Transactions on Affective Computing

In this article, we introduce a next-generation annotation tool calledNOVAfor emotional behaviour analysis, which implements a workflow that interactively incorporates the ‘human in the loop’. A main aspect of NOVA is the possibility of applying …

Unimodal Multi-Task Fusion for Emotional Mimicry Intensity Prediction

2024 · arXiv (Cornell University)

In this research, we introduce a novel methodology for assessing Emotional Mimicry Intensity (EMI) as part of the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild. Our methodology utilises the Wav2Vec 2.0 architecture, which …