Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Darío Martín-Iglesias"'
Autor:
Ascensión Gallardo-Antolín, Fernando Diaz-de-Maria, Rubén Solera-Ureña, Darío Martín-Iglesias, Carmen Peláez-Moreno
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
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The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, impor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::019637d68f58f9dc8543ee889293b0fa
https://hdl.handle.net/10016/2322
https://hdl.handle.net/10016/2322
Autor:
Darío Martín-Iglesias, Rubén Solera-Ureña, Carmen Peláez-Moreno, Fernando Diaz-de-Maria, Jaume Padrell-Sendra, Ascensión Gallardo-Antolín
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
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Lecture Notes in Computer Science ISBN: 9783540715030
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Lecture Notes in Computer Science ISBN: 9783540715030
Hidden Markov Models (HMMs) are, undoubtedly, the most employed core technique for Automatic Speech Recognition (ASR). Nevertheless, we are still far from achieving high-performance ASR systems. Some alternative approaches, most of them based on Arti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c016b44d387dedc57f79d94918be9945
https://hdl.handle.net/10016/2360
https://hdl.handle.net/10016/2360
Autor:
Ascensión Gallardo-Antolín, Darío Martín-Iglesias, J. Bernal-Chaves, Carmen Peláez-Moreno, Fernando Diaz-de-Maria
Publikováno v:
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname
Nonlinear Analyses and Algorithms for Speech Processing ISBN: 9783540312574
NOLISP
instname
Nonlinear Analyses and Algorithms for Speech Processing ISBN: 9783540312574
NOLISP
Automatic Speech Recognition (ASR) is essentially a problem of pattern classification, however, the time dimension of the speech signal has prevented to pose ASR as a simple static classification problem. Support Vector Machine (SVM) classifiers coul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::821536629ff0240878dcfe035add9b53
http://hdl.handle.net/10016/1593
http://hdl.handle.net/10016/1593