25 citations · 49 across the 10 of their papers we have counts for
10 papers
Towards Speech Impairment Prediction in German-Speaking Individuals with Amyotrophic Lateral Sclerosis
Monica Gonzalez-Machorro, Ricarda von Heynitz, Justine Hanslmeier +5
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease, often affecting speech due to bulbar dysfunction. In this study, we predict speech impairment in people with ALS…
Virtual Speech Therapist: A Clinician-in-the-Loop AI Agent for Speech Therapy Planning
Shakeel Sheikh, Patrick Marmaroli, MD Sahidullah +4
This paper presents a proof of concept of the Virtual Speech Therapist (VST), an intelligent multi-agent based system designed to support stuttering assessment and individualized t…
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…
Articulatory Feature Prediction from Surface EMG during Speech Production
Jihwan Lee, Kevin Huang, Kleanthis Avramidis +6
We present a model for predicting articulatory features from surface electromyography (EMG) signals during speech production. The proposed model integrates convolutional layers and…
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…
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…
Speech-based Age and Gender Prediction with Transformers
Felix Burkhardt, Johannes Wagner, Hagen Wierstorf +2
We report on the curation of several publicly available datasets for age and gender prediction. Furthermore, we present experiments to predict age and gender with models based on a…
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge
Andreas Triantafyllopoulos, Johannes Wagner, Hagen Wierstorf +5
Large, pre-trained neural networks consisting of self-attention layers (transformers) have recently achieved state-of-the-art results on several speech emotion recognition (SER) da…
Multistage linguistic conditioning of convolutional layers for speech emotion recognition
Andreas Triantafyllopoulos, Uwe Reichel, Shuo Liu +3
In this contribution, we investigate the effectiveness of deep fusion of text and audio features for categorical and dimensional speech emotion recognition (SER). We propose a nove…