Zobrazeno 1 - 10
of 13
pro vyhledávání: '"András Bánhalmi"'
Publikováno v:
International Journal of Transport Development and Integration. 5:15-27
Publikováno v:
CinC
The efficient detection of respiratory effort-related arousals requires enormous amount of data and a suitable learning model. Using a dataset taken from PhysioNet.org, windows of 20 seconds were extracted with their median aligned with the starting
Publikováno v:
International Journal of Speech Technology. 9:121-131
This paper examines the susceptibility of a dictation system to various types of mismatches between the training and testing conditions. With these experiments we intend to find the best training configuration for the system and also to evaluate the
Publikováno v:
ECBS-EERC
It is a frequent problem in system development when various different systems with similar functionality have to be integrated together. System integration generally means accessing, sharing and exchanging data among applications. This data sharing c
Autor:
István, Vassányi, György, Kozmann, András, Bánhalmi, Balázs, Végsö, István, Kósa, Tibor, Dulai, Zsolt, Tarjányi, Gergely, Tuboly, Péter, Cserti, Balázs, Pintér
Publikováno v:
Studies in health technology and informatics. 169
Prevention and rehabilitation efficiency can greatly benefit from the application of intelligent, 24 hour tele-diagnostics and tele-care information systems. Tele-monitoring also supports a new level of medical supervision over the patient's lifestyl
Publikováno v:
Bioinformatics Research and Applications
5th International Symposium on Bioinformatics Research and Applications (ISBRA'09)
5th International Symposium on Bioinformatics Research and Applications (ISBRA'09), May 2009, Fort Lauderdale, Florida, United States. pp.310-322
Bioinformatics Research and Applications ISBN: 9783642015502
ISBRA
5th International Symposium on Bioinformatics Research and Applications (ISBRA'09)
5th International Symposium on Bioinformatics Research and Applications (ISBRA'09), May 2009, Fort Lauderdale, Florida, United States. pp.310-322
Bioinformatics Research and Applications ISBN: 9783642015502
ISBRA
The One-Class Classification (OCC) approach is based on the assumption that samples are available only from a target class in the training phase. OCC methods have been applied with success to problems where the classes are very different in size. As
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e4c52ba97b501edc8c373feacd9fed16
https://hal.inria.fr/inria-00428924
https://hal.inria.fr/inria-00428924
Publikováno v:
Pattern Recognition and Image Analysis ISBN: 9783642021718
IbPRIA
IbPRIA
In this paper we focus on the anti-phoneme modelling part of segment-based speech recognition, where we have to distinguish the real phonemes from anything else which may appear (like parts of phonemes, several consecutive phonemes and noise). As it
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dd5184344617d5736f2c31b460df0d1e
https://doi.org/10.1007/978-3-642-02172-5_56
https://doi.org/10.1007/978-3-642-02172-5_56
Publikováno v:
Machine Learning: ECML 2007 ISBN: 9783540749578
ECML
ECML
For One-Class Classification problems several methods have been proposed in the literature. These methods all have the common feature that the decision boundary is learnt by just using a set of the positive examples. Here we propose a method that ext
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9a49f40f7f861d962f977cfe6e85d7ab
https://doi.org/10.1007/978-3-540-74958-5_51
https://doi.org/10.1007/978-3-540-74958-5_51
Publikováno v:
Text, Speech and Dialogue ISBN: 9783540746270
TSD
TSD
It is quite common to use feature extraction methods prior to classification. Here we deal with three algorithms defining uncorrelated features. The first one is the so-called whitening method, which transforms the data so that the covariance matrix
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f8b458c01cf49bf9b1f90362f54425cf
https://doi.org/10.1007/978-3-540-74628-7_30
https://doi.org/10.1007/978-3-540-74628-7_30
Publikováno v:
Text, Speech and Dialogue ISBN: 9783540746270
TSD
TSD
When training speaker-independent HMM-based acoustic models, a lot of manually transcribed acoustic training data must be available from a good many different speakers. These training databases have a great variation in the pitch of the speakers, art
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::95f384df073bd3768fc668e217ace73e
https://doi.org/10.1007/978-3-540-74628-7_50
https://doi.org/10.1007/978-3-540-74628-7_50