activity
20212026
most citedOn the Effect of Purely Synthetic Training Data for Different Automatic Speech Recognition Architectures

3 citations · 3 across the 8 of their papers we have counts for

collaborators
Showing eess.ASShow all

2 papers · 1 filter

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…

eess.AS2025

Regularizing Learnable Feature Extraction for Automatic Speech Recognition

Peter Vieting, Maximilian Kannen, Benedikt Hilmes +2

Neural front-ends are an appealing alternative to traditional, fixed feature extraction pipelines for automatic speech recognition (ASR) systems since they can be directly trained…