NDLI: Unimodal late fusion for NIST i-vector challenge on speaker detection

Content Provider	IET Digital Library
Author	Ali, Hazrat Garcez, Artur S. d' Avila Tran, Son N. Zhou, Xianwei Iqbal, Khalid
Abstract	Speaker detection is a very interesting machine learning task for which the latest i-vector challenge has been coordinated by the National Institute of Standards and Technology (NIST). A simple late fusion approach for the speaker detection task on the i-vector challenge is presented. The approach is based on the late fusion of scores from the cosine distance method (the baseline) and the scores obtained from linear discriminant analysis. The results show that by adapting the simple late fusion approach, the framework can outperform the baseline score for the decision cost function on the NIST i-vector machine learning challenge.
Starting Page	1098
Ending Page	1100
Page Count	3
ISSN	00135194
Volume Number	50
e-ISSN	1350911X
Issue Number	Issue 15, Jul (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/50/15
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2014.1207
Journal	Electronics Letters
Publisher Date	2014-07-10
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Baseline Score Cosine Distance Method DCF Decision Cost Function Knowledge Engineering Technique Learning in AI Linear Discriminant Analysis National Institute of Standards And Technology NIST I-vector Machine Learning Challenge Speaker Detection Speaker Recognition Speech Processing Technique Speech Recognition And Synthesis Unimodal Late Fusion Approach
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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