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cs.SD2025

Vocoder-Free Non-Parallel Conversion of Whispered Speech With Masked Cycle-Consistent Generative Adversarial Networks

Dominik Wagner, Ilja Baumann, Tobias Bocklet

Cycle-consistent generative adversarial networks have been widely used in non-parallel voice conversion (VC). Their ability to learn mappings between source and target features wit…

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

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

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…