20 citations · 32 across the 17 of their papers we have counts for
17 papers
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…
On the Difficulty of Token-Level Modeling of Dysfluency and Fluency Shaping Artifacts
Kashaf Gulzar, Dominik Wagner, Sebastian P. Bayerl +3
Automatic transcription of stuttered speech remains a challenge, even for modern end-to-end (E2E) automatic speech recognition (ASR) frameworks. Dysfluencies and fluency-shaping ar…
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…
MMUTF: Multimodal Multimedia Event Argument Extraction with Unified Template Filling
Philipp Seeberger, Dominik Wagner, Korbinian Riedhammer
With the advancement of multimedia technologies, news documents and user-generated content are often represented as multiple modalities, making Multimedia Event Extraction (MEE) an…
Optimized Speculative Sampling for GPU Hardware Accelerators
Dominik Wagner, Seanie Lee, Ilja Baumann +3
In this work, we optimize speculative sampling for parallel hardware accelerators to improve sampling speed. We notice that substantial portions of the intermediate matrices necess…
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…
A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem
Sebastian P. Bayerl, Dominik Wagner, Ilja Baumann +4
Most stuttering detection and classification research has viewed stuttering as a multi-class classification problem or a binary detection task for each dysfluency type; however, th…
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…
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem
Sebastian P. Bayerl, Dominik Wagner, Florian Hönig +3
Specially adapted speech recognition models are necessary to handle stuttered speech. For these to be used in a targeted manner, stuttered speech must be reliably detected. Recent…
Influence of Utterance and Speaker Characteristics on the Classification of Children with Cleft Lip and Palate
Ilja Baumann, Dominik Wagner, Franziska Braun +4
Recent findings show that pre-trained wav2vec 2.0 models are reliable feature extractors for various speaker characteristics classification tasks. We show that latent representatio…
Multi-class Detection of Pathological Speech with Latent Features: How does it perform on unseen data?
Dominik Wagner, Ilja Baumann, Franziska Braun +4
The detection of pathologies from speech features is usually defined as a binary classification task with one class representing a specific pathology and the other class representi…
Nonwords Pronunciation Classification in Language Development Tests for Preschool Children
Ilja Baumann, Dominik Wagner, Sebastian Bayerl +1
This work aims to automatically evaluate whether the language development of children is age-appropriate. Validated speech and language tests are used for this purpose to test the…
The Influence of Dataset Partitioning on Dysfluency Detection Systems
Sebastian P. Bayerl, Dominik Wagner, Elmar Nöth +2
This paper empirically investigates the influence of different data splits and splitting strategies on the performance of dysfluency detection systems. For this, we perform experim…
Detecting Vocal Fatigue with Neural Embeddings
Sebastian P. Bayerl, Dominik Wagner, Ilja Baumann +2
Vocal fatigue refers to the feeling of tiredness and weakness of voice due to extended utilization. This paper investigates the effectiveness of neural embeddings for the detection…