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Speaker-Adapted Confidence Measures for ASR using Deep Bidirectional Recurrent Neural Networks

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Speaker-Adapted Confidence Measures for ASR using Deep Bidirectional Recurrent Neural Networks

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Del Agua Teba, MA.; Giménez Pastor, A.; Sanchis Navarro, JA.; Civera Saiz, J.; Juan, A. (2018). Speaker-Adapted Confidence Measures for ASR using Deep Bidirectional Recurrent Neural Networks. IEEE/ACM Transactions on Audio Speech and Language Processing. 26(7):1198-1206. https://doi.org/10.1109/TASLP.2018.2819900

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/121369

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Title: Speaker-Adapted Confidence Measures for ASR using Deep Bidirectional Recurrent Neural Networks
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Abstract:
[EN] In the last years, Deep Bidirectional Recurrent Neural Networks (DBRNN) and DBRNN with Long Short-Term Memory cells (DBLSTM) have outperformed the most accurate classifiers for confidence estimation in automatic speech ...[+]
Subjects: Automatic speech recognition , Confidence estimation , Confidence measures , Deep bidirectional recurrent neural networks , Long short-term memory , Speaker adaptation , Speech , Adaptation models , Computer architecture , Training , Recurrent neural networks , Speech processing , Task analysis
Copyrigths: Reserva de todos los derechos
Source:
IEEE/ACM Transactions on Audio Speech and Language Processing. (issn: 2329-9290 )
DOI: 10.1109/TASLP.2018.2819900
Publisher:
Institute of Electrical and Electronics Engineers
Publisher version: http://doi.org/10.1109/TASLP.2018.2819900
Project ID: info:eu-repo/grantAgreement/EC/FP7/287755/EU info:eu-repo/grantAgreement/EC/H2020/761758/EU
Description: © 2018 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Thanks:
This work was supported in part by the European Union's Horizon 2020 research and innovation programme under Grant 761758 (X5gon), in part by the Seventh Framework Programme (FP7/2007-2013) under Grant 287755 (transLectures), ...[+]
Type: Artículo

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