- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
RWTH Aachen University, Dissertation, 2020
- Keyword
-
Speech perception
- Event
-
Veröffentlichung
- (where)
-
Aachen
- (who)
-
Universitätsbibliothek der RWTH Aachen
- (when)
-
2020
- Creator
- Contributor
- DOI
-
10.18154/RWTH-2021-01241
- URN
-
urn:nbn:de:101:1-2021081107232767245472
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:46 PM CET
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Tüske, Zoltán
- Ney, Hermann
- Heřmanský, Hynek
- Universitätsbibliothek der RWTH Aachen
Time of origin
- 2020
Other Objects (12)
Learning Acoustic Features from the Raw Waveform for Automatic Speech Recognition
Advancing neural language modeling in automatic speech recognition
Progress in Decoding for Large Vocabulary Continuous Speech Recognition
Optimization of discriminative models for speech and handwriting recognition
Data-driven deep modeling and training for automatic speech recognition
Investigation on language modelling approaches for open vocabulary speech recognition
Efficient setup of acoustic models for large vocabulary continuous speech recognition
Investigation on LSTM Recurrent N-gram Language Models for Speech Recognition
Discriminative training of linear transformations and mixture density splitting for speech recognition
Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
On using 2D sequence-to-sequence models for speech recognition
Learning Acoustic Features from the Raw Waveform for Automatic Speech Recognition
Advancing neural language modeling in automatic speech recognition
Progress in Decoding for Large Vocabulary Continuous Speech Recognition
Optimization of discriminative models for speech and handwriting recognition
Data-driven deep modeling and training for automatic speech recognition
Investigation on language modelling approaches for open vocabulary speech recognition
Efficient setup of acoustic models for large vocabulary continuous speech recognition
Investigation on LSTM Recurrent N-gram Language Models for Speech Recognition
Discriminative training of linear transformations and mixture density splitting for speech recognition
Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
On using 2D sequence-to-sequence models for speech recognition
Learning Acoustic Features from the Raw Waveform for Automatic Speech Recognition
Advancing neural language modeling in automatic speech recognition
Progress in Decoding for Large Vocabulary Continuous Speech Recognition
Optimization of discriminative models for speech and handwriting recognition
Data-driven deep modeling and training for automatic speech recognition
Investigation on language modelling approaches for open vocabulary speech recognition
Efficient setup of acoustic models for large vocabulary continuous speech recognition
Investigation on LSTM Recurrent N-gram Language Models for Speech Recognition
Discriminative training of linear transformations and mixture density splitting for speech recognition
Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition