3 papers
eess.AS2025
Unified Learnable 2D Convolutional Feature Extraction for ASR
Peter Vieting, Benedikt Hilmes, Ralf Schlüter +1
Neural front-ends represent a promising approach to feature extraction for automatic speech recognition (ASR) systems as they enable to learn specifically tailored features for dif…
cs.LG2025
Running Conventional Automatic Speech Recognition on Memristor Hardware: A Simulated Approach
Nick Rossenbach, Benedikt Hilmes, Leon Brackmann +2
Memristor-based hardware offers new possibilities for energy-efficient machine learning (ML) by providing analog in-memory matrix multiplication. Current hardware prototypes cannot…
cs.CL2023
On the Relevance of Phoneme Duration Variability of Synthesized Training Data for Automatic Speech Recognition
Nick Rossenbach, Benedikt Hilmes, Ralf Schlüter
Synthetic data generated by text-to-speech (TTS) systems can be used to improve automatic speech recognition (ASR) systems in low-resource or domain mismatch tasks. It has been sho…