NDLI: Towards building Indonesian viseme: A clustering-based approach

Content Provider	IEEE Xplore Digital Library
Author	Arifin Muljono Sumpeno, S. Hariadi, M.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. Eng., Inst. Teknol. Sepuluh Nopember Surabaya, Surabaya, Indonesia (Sumpeno, S.; Hariadi, M.) \|\| Dept. of Inf. Technol., Univ. Dian Nuswantoro, Semarang, Indonesia (Arifin; Muljono)
Abstract	Lips animation plays an important role in facial animation. A realistic lips animation requires synchronization of viseme (visual phoneme) with the spoken phonemes. This research aims towards building Indonesian viseme by configuring viseme classes based on the clustering process result of visual speech images data. The research used Subspace LDA, which is a combination of Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA), as the extraction feature method. The Subspace LDA method is expected to be able to produce an optimal dimension reduction. The clustering process utilized K-Means algorithms to split data into a number of clusters. The quality of clustering result is measured by using Sum of Squared Error (SSE) and a ratio of Between-Class Variation (BCV) and Within-Class Variation (WCV). From these measurements, we found that the best quality clustering occurs at k=9. The finding of this research is the Indonesian viseme consisting of 10 classes (9 classes of clustering result and one neutral class). For a future work, the result of this research can be used as a reference to the Indonesian viseme structure that is defined based on linguistic knowledge.
Sponsorship	Indonesia Sect.
Starting Page	57
Ending Page	61
File Size	3592501
Page Count	5
File Format	PDF
ISBN	9781467360531
DOI	10.1109/CyberneticsCom.2013.6865781
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-03
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Viseme Lips Sum of Squared Error K-Means Subspace LDA Speech Animation Feature extraction Clustering Covariance matrices Principal component analysis
Content Type	Text
Resource Type	Article

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