NDLI: PLF Optimization for Target Language Detection

Content Provider	IET Digital Library
Author	Zhang, Jian Yuan, Qingsheng Bao, Xiuguo Zhou, Ruohua Yan, Yonghong
Abstract	The objective of traditional feature studies in Spoken language recognition (SLR) is extracting the linguistic discrimination between each language. However, applications of security area always interested in a particular language, which requires the features should be the best reflection of the differences between target language and the other languages. To address this problems, the frame level Phone log-posteriors feature (PLF), which has been recently introduced as a novel and effective feature in SLR, is optimized to get a better performance on Target language detection (TLD) task. The F-Ratio analysis method is used to analyze the contribution of each dimension in feature vector for TLD. In this work, frame level phone posterior probabilities are estimated by a phone recognizer, and processed through taking logarithm. Then the feature is optimized through weighting each dimension according to the F-Ratio values. Finally, Principal component analysis (PCA) is used to decorrelate the feature and reduce vector size. Experiments carried out on the NIST LRE 2007 dataset show that the effectiveness of the optimized feature, which yields significant relative improvements in term of Equal error rate (EER) with regard to the Gaussian mixture models-Support vector machines (GMM-SVM) system based on the original feature.
Starting Page	118
Ending Page	121
Page Count	4
ISSN	10224653
Volume Number	26
e-ISSN	20755597
Issue Number	Issue 1, Jan (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/26/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2016.11.014
Journal	Chinese Journal of Electronics
Publisher Date	2017-01-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	EER Equal Error Rate F-ratio Analysis Method Feature Vector Frame Level Phone Posterior Probabilities Gaussian Mixture Model-support Vector Machine Gaussian Processes GMM-SVM System Knowledge Engineering Technique Mixture Model PCA Phone Log-posteriors Feature Phone Recognizer PLF Optimization Principal Component Analysis SLR Speech Processing Technique Speech Recognition Speech Recognition And Synthesis Spoken Language Recognition Statistics Support Vector Machine Target Language Detection TLD
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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