TELKOMNIKA Telecommunication, Computing, Electronics and Control
Kernal based speaker specific feature extraction and its applications in iTaukei cross language speaker recognition
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Kernal based speaker specific feature extraction and its applications in iTaukei cross language speaker recognition
Kernal based speaker specific feature extraction and its applications in iTaukei cross language speaker recognition
Subject
Automatic speaker recognition system, Kernel independent component
analysis, Kernel linear discriminant analysis, Kernel principal component
analysis, Principal component analysis
analysis, Kernel linear discriminant analysis, Kernel principal component
analysis, Principal component analysis
Description
Extraction and classification algorithms based on kernel nonlinear features are popular in the new direction of research in machine learning. This research paper considers their practical application in the iTaukei automatic speaker recognition system (ASR) for cross-language speech recognition. Second, nonlinear speaker-specific extraction methods such as kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and kernel linear discriminant analysis (KLDA) are summarized. The conversion effects on subsequent classifications were tested in conjunction with Gaussian mixture modeling (GMM) learning algorithms; in most cases, computations were found to have a beneficial effect on classification performance. Additionally, the best results were achieved by the Kernel linear discriminant analysis (KLDA) algorithm. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using ATR Japanese C language corpus and self-recorded iTaukei corpus. The ASR efficiency of KLDA, KICA, and KLDA technique for 6 sec of ATR Japanese C language corpus 99.7%, 99.6%, and 99.1% and equal error
rate (EER) are 1.95%, 2.31%, and 3.41% respectively. The EER improvement of the KLDA technique-based ASR system compared with KICA and KPCA is 4.25% and 8.51% respectively.
rate (EER) are 1.95%, 2.31%, and 3.41% respectively. The EER improvement of the KLDA technique-based ASR system compared with KICA and KPCA is 4.25% and 8.51% respectively.
Creator
Satyanand Singh, Pragya Singh
Source
DOI: 10.12928/TELKOMNIKA.v18i5.14655
Publisher
Universitas Ahmad Dahlan
Date
October 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Satyanand Singh, Pragya Singh, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Kernal based speaker specific feature extraction and its applications in iTaukei cross language speaker recognition,” Repository Horizon University Indonesia, accessed March 10, 2025, https://repository.horizon.ac.id/items/show/4073.
Kernal based speaker specific feature extraction and its applications in iTaukei cross language speaker recognition,” Repository Horizon University Indonesia, accessed March 10, 2025, https://repository.horizon.ac.id/items/show/4073.