348 citations · 467 across the 16 of their papers we have counts for
3 papers · 1 filter
Testing Correctness, Fairness, and Robustness of Speech Emotion Recognition Models
Anna Derington, Hagen Wierstorf, Ali Özkil +3
Machine learning models for speech emotion recognition (SER) can be trained for different tasks and are usually evaluated based on a few available datasets per task. Tasks could in…
audb -- Sharing and Versioning of Audio and Annotation Data in Python
Hagen Wierstorf, Johannes Wagner, Florian Eyben +2
Driven by the need for larger and more diverse datasets to pre-train and fine-tune increasingly complex machine learning models, the number of datasets is rapidly growing. audb is…
Dawn of the transformer era in speech emotion recognition: closing the valence gap
Johannes Wagner, Andreas Triantafyllopoulos, Hagen Wierstorf +4
Recent advances in transformer-based architectures which are pre-trained in self-supervised manner have shown great promise in several machine learning tasks. In the audio domain,…