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Your search keyword '"Morales-Cordovilla, Juan A."' showing total 27 results
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27 results on '"Morales-Cordovilla, Juan A."'

1. CVX-Optimized Beamforming and Vector Taylor Series Compensation with German ASR Employing Star-Shaped Microphone Array

Author

Morales-Cordovilla, Juan A., Pessentheiner, Hannes, Hagmüller, Martin, González, José A., Kubin, Gernot, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Navarro Mesa, Juan Luis, editor, Ortega, Alfonso, editor, Teixeira, António, editor, Hernández Pérez, Eduardo, editor, Quintana Morales, Pedro, editor, Ravelo García, Antonio, editor, Guerra Moreno, Iván, editor, and Toledano, Doroteo T., editor

Published
2014
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9. Técnicas de reconocimiento robusto de la voz basadas en el pitch

Author

Morales Cordovilla, Juan Andrés, Peinado Herrera, Antonio Miguel, Sánchez Calle, Victoria, Universidad de Granada. Departamento de Teoría de la Señal Telemática y Comunicaciones, and Sánchez Calle, Victoria Eugenia

Subjects
Sistemas de procesamiento de la voz, 681.3, Reconocimiento automático del habla, 621.39
Abstract

Tesis Univ. Granada. Departamento de Teoría de la Señal Telemática y Comunicaciones. Leída el 5 de septiembre de 2011

Published
2018

10. Protein alignment based on higher order conditional random fields for template-based modeling

Author

Morales Cordovilla, Juan Andrés, Sánchez Calle, Victoria Eugenia, and Ratajczak, Martin

Abstract

The query-template alignment of proteins is one of the most critical steps of template-based modeling methods used to predict the 3D structure of a query protein. This alignment can be interpreted as a temporal classification or structured prediction task and first order Conditional Random Fields have been proposed for protein alignment and proven to be rather successful. Some other popular structured prediction problems, such as speech or image classification, have gained from the use of higher order Conditional Random Fields due to the well known higher order correlations that exist between their labels and features. In this paper, we propose and describe the use of higher order Conditional Random Fields for query-template protein alignment. The experiments carried out on different public datasets validate our proposal, especially on distantly-related protein pairs which are the most difficult to align., This research was supported by Project P12.TIC.1485 funded by Consejeria de Economia, Innovacion y Ciencia (Junta de Andalucia) and Spanish MINECO/FEDER Project TEC2016-80141- P.

Published
2018

16. What we can learn from asr errors about low-resourced languages: a case-study of luxembourgish and austrian

Author

Adda-Decker, Martine, Schuppler, Barbara, Lamel, Lori, Morales Cordovilla, Juan Andrés, Adda, Gilles, LPP - Laboratoire de Phonétique et Phonologie - UMR 7018 (LPP), Université Sorbonne Nouvelle - Paris 3-Centre National de la Recherche Scientifique (CNRS), Graz University of Technology [Graz] (TU Graz), Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Université Paris-Sud - Paris 11 (UP11), Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Institut des Technologies Multilingues et Multimédias de l'Information (IMMI), Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Université Paris-Sud - Paris 11 (UP11)-Sorbonne Université - UFR d'Ingénierie (UFR 919), and Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Saclay (COmUE)

Subjects
low-resourced languages, [SHS.LANGUE]Humanities and Social Sciences/Linguistics, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2013

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