Learning Acoustic Features from the Raw Waveform for Automatic Speech Recognition

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Notes: In: Berlin : Deutsche Gesellschaft für Akustik e.V. (DEGA), 2018, Seiten/Artikel-Nr:1533-1536

Event: Veröffentlichung

(where): Aachen

(who): Universitätsbibliothek der RWTH Aachen

(when): 2018

Creator: Menne, Tobias
Tüske, Zoltán
Schlüter, Ralf
Ney, Hermann

DOI: 10.18154/RWTH-CONV-236778

URN: urn:nbn:de:101:1-2019090508472166558789

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:26 AM CEST

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Associated

Menne, Tobias
Tüske, Zoltán
Schlüter, Ralf
Ney, Hermann
Universitätsbibliothek der RWTH Aachen

Time of origin

2018

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Learning Acoustic Features from the Raw Waveform for Automatic Speech Recognition

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Other Objects (12)

Efficient setup of acoustic models for large vocabulary continuous speech recognition

Investigation into Joint Optimization of Single Channel Speech Enhancement and Acoustic Modeling for Robust ASR

Investigations on acoustic model speaker adaptation

Discriminative feature modeling for statistical speech recognition

Advancing neural language modeling in automatic speech recognition

Towards online-recognition with deep bidirectional LSTM acoustic models

Progress in Decoding for Large Vocabulary Continuous Speech Recognition

Optimization of discriminative models for speech and handwriting recognition

Investigation on language modelling approaches for open vocabulary speech recognition

Data-driven deep modeling and training for automatic 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

Efficient setup of acoustic models for large vocabulary continuous speech recognition

Investigation into Joint Optimization of Single Channel Speech Enhancement and Acoustic Modeling for Robust ASR

Investigations on acoustic model speaker adaptation

Discriminative feature modeling for statistical speech recognition

Advancing neural language modeling in automatic speech recognition

Towards online-recognition with deep bidirectional LSTM acoustic models

Progress in Decoding for Large Vocabulary Continuous Speech Recognition

Optimization of discriminative models for speech and handwriting recognition

Investigation on language modelling approaches for open vocabulary speech recognition

Data-driven deep modeling and training for automatic 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

Efficient setup of acoustic models for large vocabulary continuous speech recognition

Investigation into Joint Optimization of Single Channel Speech Enhancement and Acoustic Modeling for Robust ASR

Investigations on acoustic model speaker adaptation

Discriminative feature modeling for statistical speech recognition

Advancing neural language modeling in automatic speech recognition

Towards online-recognition with deep bidirectional LSTM acoustic models

Progress in Decoding for Large Vocabulary Continuous Speech Recognition

Optimization of discriminative models for speech and handwriting recognition

Investigation on language modelling approaches for open vocabulary speech recognition

Data-driven deep modeling and training for automatic 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

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