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Notice of Violation of IEEE Publication Principles
Acoustic and Phoneme Modeling Based on Confusion Matrix for Ubiquitous Mixed-Language Speech Recognition

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6 Author(s)
Po-Yi Shih ; Dept. of Electr. Eng., Nat. Cheng Rung Univ., Tainan ; Jhing-Fa Wang ; Hsiao-Ping Lee ; Hung-Jen Kai
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Notice of Violation of IEEE Publication Principles

"Acoustic and Phoneme Modeling Based on Confusion Matrix for Ubiquitous Mixed-Language Speech Recognition,"
by Po-Yi Shih; Jhing-Fa Wang; Hsiao-Ping Lee; Hung-Jen Kai; Hung-Tzu Kao; Yuan-Ning Lin,
in the Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous and Trustworthy Computing, 2008. SUTC '08, pp. 500-506, June 2008

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"Dissimilarity Measures for Hidden Markov Models and Their Application in Multilingual Speech Recognition"
by Matti Vihola
in his Masters Thesis, Tampere University of Technology, November 2001

This work presents a novel approach to acoustic and phoneme modeling in order to recognize ubiquitous mixed-language speech. The conventional approaches to perform multilingual speech recognition are the usage of a multilingual phone set. A confusion matrix combining acoustic between every two phonetic is built for phonetic unit clustering. In this work, we are interested in speaker independent voice command recognition. The IPA representation is adapted for phonetic unit modeling. The EAT is applied to construct speaker independent acoustic models. The experimental results show that the proposed method can perform 70-80% lexicon recognition accuracy.

Published in:

Sensor Networks, Ubiquitous and Trustworthy Computing, 2008. SUTC '08. IEEE International Conference on

Date of Conference:

11-13 June 2008