20 citations · 49 across the 34 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
Self-Supervised Adaptive AV Fusion Module for Pre-Trained ASR Models
Christopher Simic, Tobias Bocklet
Automatic speech recognition (ASR) has reached a level of accuracy in recent years, that even outperforms humans in transcribing speech to text. Nevertheless, all current ASR appro…
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…
Generative Models for Improved Naturalness, Intelligibility, and Voicing of Whispered Speech
Dominik Wagner, Sebastian P. Bayerl, Hector A. Cordourier Maruri +1
This work adapts two recent architectures of generative models and evaluates their effectiveness for the conversion of whispered speech to normal speech. We incorporate the normal…
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…