15 citations · 20 across the 5 of their papers we have counts for
7 papers
Self-Supervised Speech Representations Preserve Speech Characteristics while Anonymizing Voices
Abner Hernandez, Paula Andrea Pérez-Toro, Juan Camilo Vásquez-Correa +3
Collecting speech data is an important step in training speech recognition systems and other speech-based machine learning models. However, the issue of privacy protection is an in…
Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition
Abner Hernandez, Paula Andrea Pérez-Toro, Elmar Nöth +3
State-of-the-art automatic speech recognition (ASR) systems perform well on healthy speech. However, the performance on impaired speech still remains an issue. The current study ex…
The effect of speech pathology on automatic speaker verification -- a large-scale study
Soroosh Tayebi Arasteh, Tobias Weise, Maria Schuster +3
Navigating the challenges of data-driven speech processing, one of the primary hurdles is accessing reliable pathological speech data. While public datasets appear to offer solutio…
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…
CLC: Complex Linear Coding for the DNS 2020 Challenge
Hendrik Schröter, Tobias Rosenkranz, Alberto N. Escalante-B. +1
Complex-valued processing brought deep learning-based speech enhancement and signal extraction to a new level. Typically, the noise reduction process is based on a time-frequency (…
Lightweight Online Noise Reduction on Embedded Devices using Hierarchical Recurrent Neural Networks
Hendrik Schröter, Tobias Rosenkranz, Alberto N. Escalante-B. +2
Deep-learning based noise reduction algorithms have proven their success especially for non-stationary noises, which makes it desirable to also use them for embedded devices like h…