activity
20192026
most citedDetecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0

47 citations · 143 across the 42 of their papers we have counts for

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11 papers · 1 filter

cs.SD2026

Time vs. Layer: Locating Predictive Cues for Dysarthric Speech Descriptors in wav2vec 2.0

Natalie Engert, Dominik Wagner, Korbinian Riedhammer +1

Wav2vec 2.0 (W2V2) has shown strong performance in pathological speech analysis by effectively capturing the characteristics of atypical speech. Despite its success, it remains unc…

cs.SD2025

Shared Multi-modal Embedding Space for Face-Voice Association

Christopher Simic, Korbinian Riedhammer, Tobias Bocklet

The FAME 2026 challenge comprises two demanding tasks: training face-voice associations combined with a multilingual setting that includes testing on languages on which the model w…

cs.SD2025

Personalized Fine-Tuning with Controllable Synthetic Speech from LLM-Generated Transcripts for Dysarthric Speech Recognition

Dominik Wagner, Ilja Baumann, Natalie Engert +4

In this work, we present our submission to the Speech Accessibility Project challenge for dysarthric speech recognition. We integrate parameter-efficient fine-tuning with latent au…

cs.SD2025

Adapter-Based Multi-Agent AVSR Extension for Pre-Trained ASR Models

Christopher Simic, Korbinian Riedhammer, Tobias Bocklet

We present an approach to Audio-Visual Speech Recognition that builds on a pre-trained Whisper model. To infuse visual information into this audio-only model, we extend it with an…

cs.SD2025

Optimized Self-supervised Training with BEST-RQ for Speech Recognition

Ilja Baumann, Dominik Wagner, Korbinian Riedhammer +1

Self-supervised learning has been successfully used for various speech related tasks, including automatic speech recognition. BERT-based Speech pre-Training with Random-projection…

cs.SD2024

Large Language Models for Dysfluency Detection in Stuttered Speech

Dominik Wagner, Sebastian P. Bayerl, Ilja Baumann +3

Accurately detecting dysfluencies in spoken language can help to improve the performance of automatic speech and language processing components and support the development of more…

cs.SD2024

Outlier Reduction with Gated Attention for Improved Post-training Quantization in Large Sequence-to-sequence Speech Foundation Models

Dominik Wagner, Ilja Baumann, Korbinian Riedhammer +1

This paper explores the improvement of post-training quantization (PTQ) after knowledge distillation in the Whisper speech foundation model family. We address the challenge of outl…

cs.SD2024★ 1 cited

A Survey of Music Generation in the Context of Interaction

Ismael Agchar, Ilja Baumann, Franziska Braun +4

In recent years, machine learning, and in particular generative adversarial neural networks (GANs) and attention-based neural networks (transformers), have been successfully used t…

cs.SD2022

Speaker Adaptation for End-To-End Speech Recognition Systems in Noisy Environments

Dominik Wagner, Ilja Baumann, Sebastian P. Bayerl +2

We analyze the impact of speaker adaptation in end-to-end automatic speech recognition models based on transformers and wav2vec 2.0 under different noise conditions. By including s…

cs.SD2022★ 5 cited

Going Beyond the Cookie Theft Picture Test: Detecting Cognitive Impairments using Acoustic Features

Franziska Braun, Andreas Erzigkeit, Hartmut Lehfeld +3

Standardized tests play a crucial role in the detection of cognitive impairment. Previous work demonstrated that automatic detection of cognitive impairment is possible using audio…

cs.SD2022★ 1 cited

The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes

Björn W. Schuller, Anton Batliner, Shahin Amiriparian +12

The ACM Multimedia 2022 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the…