activity
20182026
most citedImproved training of end-to-end attention models for speech recognition

282 citations · 700 across the 18 of their papers we have counts for

collaborators
Showing 2021Show all

5 papers · 1 filter

cs.CL2021

Automatic Learning of Subword Dependent Model Scales

Felix Meyer, Wilfried Michel, Mohammad Zeineldeen +2

To improve the performance of state-of-the-art automatic speech recognition systems it is common practice to include external knowledge sources such as language models or prior cor…

cs.CL2021

On Sampling-Based Training Criteria for Neural Language Modeling

Yingbo Gao, David Thulke, Alexander Gerstenberger +3

As the vocabulary size of modern word-based language models becomes ever larger, many sampling-based training criteria are proposed and investigated. The essence of these sampling…

cs.CL2021

The Impact of ASR on the Automatic Analysis of Linguistic Complexity and Sophistication in Spontaneous L2 Speech

Yu Qiao, Wei Zhou, Elma Kerz +1

In recent years, automated approaches to assessing linguistic complexity in second language (L2) writing have made significant progress in gauging learner performance, predicting h…

cs.CL2021

Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures

Nick Rossenbach, Mohammad Zeineldeen, Benedikt Hilmes +2

Recent publications on automatic-speech-recognition (ASR) have a strong focus on attention encoder-decoder (AED) architectures which tend to suffer from over-fitting in low resourc…

eess.AS2021

On Architectures and Training for Raw Waveform Feature Extraction in ASR

Peter Vieting, Christoph Lüscher, Wilfried Michel +2

With the success of neural network based modeling in automatic speech recognition (ASR), many studies investigated acoustic modeling and learning of feature extractors directly bas…