Feature selection methods for hidden Markov model-based speech recognition

Simple item page
dc.contributor.authorNouza, Jan
dc.date.accessioned2016-07-08
dc.date.available2016-07-08
dc.date.issued1996
dc.description.abstractIn the paper three different feature selection methods applicable to speech recognition are presented and discussed. Widely known approaches, like the principal component analysis, discriminant feature analysis and sequential search methods, have been customised for the use with a hidden Markov model based classifier. When comparing the methods we focus mainly on their ability to reduce the size of the feature vectors standardly used in speech processing. It is demonstrated that the sequential methods and the discriminative analysis are well suited for that task. Both of them may contribute to a recognition time reduction by a factor higher than two without a significant loss of accuracy, particularly, in the combination with a two-level classification scheme. © 1996 IEEE.en
dc.formattext
dc.identifier.doi10.1109/ICPR.1996.546749
dc.identifier.isbn081867282X
dc.identifier.isbn9780818672828
dc.identifier.issn1051-4651
dc.identifier.scopus2-s2.0-33645191851
dc.identifier.urihttps://dspace.tul.cz/handle/15240/16588
dc.identifier.urihttps://ieeexplore.ieee.org/document/546749
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofProceedings - International Conference on Pattern Recognition
dc.sourced-scopus
dc.titleFeature selection methods for hidden Markov model-based speech recognitionen
dc.typeconferenceObject
local.accessaccess
local.citation.epage190
local.citation.spage186
local.departmentDepartment of Electronics and Signal Processing
local.event.edate1996-08-29
local.event.locationVienna
local.event.sdate1996-08-25
local.event.title13th International Conference on Pattern Recognition, ICPR 1996
local.facultyFaculty of Mechatronic and Interdisciplinary Studies
local.fulltextyes
local.fulltextyes
local.identifier.codenPICRE
local.notenefunguje RIV
local.relation.volume2
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
2-s2.0-33645191851-a.pdf
Size:
477.6 KB
Format:
Adobe Portable Document Format
Description:
Článek
Download
Collections