Hochschulschrift
Investigation on language modelling approaches for open vocabulary speech recognition
- Alternative title
-
Untersuchung von Methoden der Sprachmodellierung für die Spracherkennung von offenem Vokabular
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
-
RWTH Aachen University, Dissertation, 2016
- Classification
-
Informatik
- Event
-
Veröffentlichung
- (where)
-
Aachen
- (who)
-
Universitätsbibliothek der RWTH Aachen
- (when)
-
2016
- Creator
- Contributor
- URN
-
urn:nbn:de:hbz:82-rwth-2017-016452
- 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.
Object type
- Hochschulschrift
Associated
- Basha Shaik, Mahaboob Ali
- Ney, Hermann
- Höge, Harald
- Universitätsbibliothek der RWTH Aachen
Time of origin
- 2016
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Optimization of discriminative models for speech and handwriting recognition
Data-driven deep modeling and training for automatic speech recognition
Towards large vocabulary continuous sign language recognition: from artificial to real-life tasks
Investigation on LSTM Recurrent N-gram Language Models for Speech Recognition
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Progress in Decoding for Large Vocabulary Continuous Speech Recognition
Efficient setup of acoustic models for large vocabulary continuous speech recognition
Segmental Encoder-Decoder Models for Large Vocabulary Automatic Speech Recognition
Discriminative feature modeling for statistical speech recognition
Advancing neural language modeling in automatic speech recognition
Optimization of discriminative models for speech and handwriting recognition
Data-driven deep modeling and training for automatic speech recognition
Towards large vocabulary continuous sign language recognition: from artificial to real-life tasks
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
Progress in Decoding for Large Vocabulary Continuous Speech Recognition
Efficient setup of acoustic models for large vocabulary continuous speech recognition
Segmental Encoder-Decoder Models for Large Vocabulary Automatic Speech Recognition
Discriminative feature modeling for statistical speech recognition
Advancing neural language modeling in automatic speech recognition
Optimization of discriminative models for speech and handwriting recognition
Data-driven deep modeling and training for automatic speech recognition
Towards large vocabulary continuous sign language recognition: from artificial to real-life tasks
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