Language:
English
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Language Identification Using Spectr...
~
SpringerLink (Online service)
Language Identification Using Spectral and Prosodic Features
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Language Identification Using Spectral and Prosodic Features/ by K. Sreenivasa Rao, V. Ramu Reddy, Sudhamay Maity.
Author:
Rao, K. Sreenivasa.
other author:
Reddy, V. Ramu.
Description:
XI, 98 p. 21 illus., 5 illus. in color.online resource. :
Contained By:
Springer Nature eBook
Subject:
Signal processing. -
Online resource:
https://doi.org/10.1007/978-3-319-17163-0
ISBN:
9783319171630
Language Identification Using Spectral and Prosodic Features
Rao, K. Sreenivasa.
Language Identification Using Spectral and Prosodic Features
[electronic resource] /by K. Sreenivasa Rao, V. Ramu Reddy, Sudhamay Maity. - 1st ed. 2015. - XI, 98 p. 21 illus., 5 illus. in color.online resource. - SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,2191-737X. - SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,.
Introduction.- Literature Review -- Language Identification using Spectral Features -- Language Identification using Prosodic Features -- Summary and Conclusions -- Appendix A: LPCC Features -- Appendix B: MFCC Features -- Appendix C: Gaussian Mixture Model (GMM).
This book discusses the impact of spectral features extracted from frame level, glottal closure regions, and pitch-synchronous analysis on the performance of language identification systems. In addition to spectral features, the authors explore prosodic features such as intonation, rhythm, and stress features for discriminating the languages. They present how the proposed spectral and prosodic features capture the language specific information from two complementary aspects, showing how the development of language identification (LID) system using the combination of spectral and prosodic features will enhance the accuracy of identification as well as improve the robustness of the system. This book provides the methods to extract the spectral and prosodic features at various levels, and also suggests the appropriate models for developing robust LID systems according to specific spectral and prosodic features. Finally, the book discuss about various combinations of spectral and prosodic features, and the desired models to enhance the performance of LID systems.
ISBN: 9783319171630
Standard No.: 10.1007/978-3-319-17163-0doiSubjects--Topical Terms:
561459
Signal processing.
LC Class. No.: TK5102.9
Dewey Class. No.: 621.382
Language Identification Using Spectral and Prosodic Features
LDR
:02877nam a22004215i 4500
001
960944
003
DE-He213
005
20200703130947.0
007
cr nn 008mamaa
008
201211s2015 gw | s |||| 0|eng d
020
$a
9783319171630
$9
978-3-319-17163-0
024
7
$a
10.1007/978-3-319-17163-0
$2
doi
035
$a
978-3-319-17163-0
050
4
$a
TK5102.9
050
4
$a
TA1637-1638
072
7
$a
TTBM
$2
bicssc
072
7
$a
TEC008000
$2
bisacsh
072
7
$a
TTBM
$2
thema
072
7
$a
UYS
$2
thema
082
0 4
$a
621.382
$2
23
100
1
$a
Rao, K. Sreenivasa.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
891982
245
1 0
$a
Language Identification Using Spectral and Prosodic Features
$h
[electronic resource] /
$c
by K. Sreenivasa Rao, V. Ramu Reddy, Sudhamay Maity.
250
$a
1st ed. 2015.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2015.
300
$a
XI, 98 p. 21 illus., 5 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,
$x
2191-737X
505
0
$a
Introduction.- Literature Review -- Language Identification using Spectral Features -- Language Identification using Prosodic Features -- Summary and Conclusions -- Appendix A: LPCC Features -- Appendix B: MFCC Features -- Appendix C: Gaussian Mixture Model (GMM).
520
$a
This book discusses the impact of spectral features extracted from frame level, glottal closure regions, and pitch-synchronous analysis on the performance of language identification systems. In addition to spectral features, the authors explore prosodic features such as intonation, rhythm, and stress features for discriminating the languages. They present how the proposed spectral and prosodic features capture the language specific information from two complementary aspects, showing how the development of language identification (LID) system using the combination of spectral and prosodic features will enhance the accuracy of identification as well as improve the robustness of the system. This book provides the methods to extract the spectral and prosodic features at various levels, and also suggests the appropriate models for developing robust LID systems according to specific spectral and prosodic features. Finally, the book discuss about various combinations of spectral and prosodic features, and the desired models to enhance the performance of LID systems.
650
0
$a
Signal processing.
$3
561459
650
0
$a
Image processing.
$3
557495
650
0
$a
Speech processing systems.
$3
564428
650
0
$a
Natural language processing (Computer science).
$3
802180
650
0
$a
Computational linguistics.
$3
555811
650
1 4
$a
Signal, Image and Speech Processing.
$3
670837
650
2 4
$a
Natural Language Processing (NLP).
$3
1254293
650
2 4
$a
Computational Linguistics.
$3
670080
700
1
$a
Reddy, V. Ramu.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1255127
700
1
$a
Maity, Sudhamay.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1255128
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783319171623
776
0 8
$i
Printed edition:
$z
9783319171647
830
0
$a
SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,
$x
2191-737X
$3
1255129
856
4 0
$u
https://doi.org/10.1007/978-3-319-17163-0
912
$a
ZDB-2-ENG
912
$a
ZDB-2-SXE
950
$a
Engineering (SpringerNature-11647)
950
$a
Engineering (R0) (SpringerNature-43712)
based on 0 review(s)
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login