103 citations · 252 across the 15 of their papers we have counts for
7 papers · 2 filters
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…
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…
Representation Learning Strategies to Model Pathological Speech: Effect of Multiple Spectral Resolutions
Gabriel Figueiredo Miller, Juan Camilo Vásquez-Correa, Juan Rafael Orozco-Arroyave +1
This paper considers a representation learning strategy to model speech signals from patients with Parkinson's disease and cleft lip and palate. In particular, it compares differen…
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…
Disentangled Latent Speech Representation for Automatic Pathological Intelligibility Assessment
Tobias Weise, Philipp Klumpp, Kubilay Can Demir +5
Speech intelligibility assessment plays an important role in the therapy of patients suffering from pathological speech disorders. Automatic and objective measures are desirable to…
Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0
Sebastian P. Bayerl, Dominik Wagner, Elmar Nöth +1
Stuttering is a varied speech disorder that harms an individual's communication ability. Persons who stutter (PWS) often use speech therapy to cope with their condition. Improving…
Common Phone: A Multilingual Dataset for Robust Acoustic Modelling
Philipp Klumpp, Tomás Arias-Vergara, Paula Andrea Pérez-Toro +2
Current state of the art acoustic models can easily comprise more than 100 million parameters. This growing complexity demands larger training datasets to maintain a decent general…