6 papers
Speech-Based Depressive Mood Detection in the Presence of Multiple Sclerosis: A Cross-Corpus and Cross-Lingual Study
Monica Gonzalez-Machorro, Uwe Reichel, Pascal Hecker +5
Depression commonly co-occurs with neurodegenerative disorders like Multiple Sclerosis (MS), yet the potential of speech-based Artificial Intelligence for detecting depression in s…
Am I Blue or Is My Hobby Counting Teardrops? Expression Leakage in Large Language Models as a Symptom of Irrelevancy Disruption
Berkay Köprü, Mehrzad Mashal, Yigit Gurses +6
Large language models (LLMs) have advanced natural language processing (NLP) skills such as through next-token prediction and self-attention, but their ability to integrate broad c…
Testing Correctness, Fairness, and Robustness of Speech Emotion Recognition Models
Anna Derington, Hagen Wierstorf, Ali Ãzkil +3
Machine learning models for speech emotion recognition (SER) can be trained for different tasks and are usually evaluated based on a few available datasets per task. Tasks could in…
Wav2Small: Distilling Wav2Vec2 to 72K parameters for Low-Resource Speech emotion recognition
Dionyssos Kounadis-Bastian, Oliver Schrüfer, Anna Derington +4
Speech Emotion Recognition (SER) needs high computational resources to overcome the challenge of substantial annotator disagreement. Today SER is shifting towards dimensional annot…
Using voice analysis as an early indicator of risk for depression in young adults
Klaus R. Scherer, Felix Burkhardt, Uwe D. Reichel +2
Increasingly frequent publications in the literature report voice quality differences between depressed patients and controls. Here, we examine the possibility of using voice analy…
Are you sure? Analysing Uncertainty Quantification Approaches for Real-world Speech Emotion Recognition
Oliver Schrüfer, Manuel Milling, Felix Burkhardt +2
Uncertainty Quantification (UQ) is an important building block for the reliable use of neural networks in real-world scenarios, as it can be a useful tool in identifying faulty pre…