R
Rubén San-Segundo
Researcher at Technical University of Madrid
Publications - 91
Citations - 1912
Rubén San-Segundo is an academic researcher from Technical University of Madrid. The author has contributed to research in topics: Sign language & Word error rate. The author has an hindex of 20, co-authored 87 publications receiving 1484 citations. Previous affiliations of Rubén San-Segundo include University of Colorado Boulder.
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Proceedings ArticleDOI
Confidence measures for dialogue management in the CU Communicator system
TL;DR: A combined measure of confidence that utilizes the language model back-off sequence, language model score, and phonetic length of recognized words as indicators of speech recognition confidence is considered.
Journal ArticleDOI
A project-based learning approach to design electronic systems curricula
Javier Macias-Guarasa,Juan Manuel Montero,Rubén San-Segundo,Alvaro Araujo,Octavio Nieto-Taladriz +4 more
TL;DR: An important result is that all students have developed more complex and sophisticated electronic systems, while considering that the results are worth the effort invested.
Journal ArticleDOI
Classification of epileptic EEG recordings using signal transforms and convolutional neural networks
TL;DR: This analysis was carried out using two public datasets (Bern-Barcelona EEG and Epileptic Seizure Recognition datasets) obtaining significant improvements in accuracy.
Journal ArticleDOI
Speech to sign language translation system for Spanish
Rubén San-Segundo,R. Barra,Ricardo de Córdoba,Luis Fernando D'Haro,F. Fernández,Javier Ferreiros,J. M. Lucas,Javier Macias-Guarasa,Juan Manuel Montero,José Manuel Pardo +9 more
TL;DR: The development of and the first experiments in a Spanish to sign language translation system in a real domain focusing on the sentences spoken by an official when assisting people applying for, or renewing their Identity Card are described.
Journal ArticleDOI
Feature extraction from smartphone inertial signals for human activity segmentation
Rubén San-Segundo,Juan Manuel Montero,Roberto Barra-Chicote,Fernando Fernández,José Manuel Pardo +4 more
TL;DR: Adapted MFCC and PLP coefficients improve human activity recognition and segmentation accuracies while reducing feature vector size considerably, overcome significantly baseline error rates and contribute significantly to reduce the segmentation error rate.