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Content Provider | IEEE Xplore Digital Library |
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Author | Zeliang Zhang Xiongfei Li |
Copyright Year | 2011 |
Description | Author affiliation: Key Laboratory of Symbol Computation and Knowledge, Engineering of Ministry of Education, Jilin University, Changchun, China (Zeliang Zhang; Xiongfei Li) |
Abstract | Visual voice lip-reading, so the computer can understand what the speakers want to express direction by looking at their lips. Lip reading is the easiest way to compare the early characters and templates from the frozen image is stored. It ignores the very nature and time changes. This method is very simple, but it's just simple elements can be classified, then it may not show significant speech recognition services. Behavior was characterized by more and more common. Because of the hidden Markov model is superior (HMM), which can be widely used in speech recognition. In recent years, is also used to lip reading identification. Classical HMM model, so that the two assumptions: hidden assumptions collected: in t+1 the state can only be in this country is not in the state before t; from the hidden visible state hypothesis: only by regulating the t hide the visible state, rather than the previous state. This hypothesis is not very useful in some applications (such as lip reading) is reasonable. Under certain conditions, in the t state not only limits the t−1, but also t−2. Therefore, this study modified the assumptions of the classical HMM to derive a new HMM model and algorithms, and applied to the lip-reading recognition is increasing discrimination. |
Starting Page | 1996 |
Ending Page | 1999 |
File Size | 239369 |
Page Count | 4 |
File Format | |
ISBN | 9781424497621 |
e-ISBN | 9781424497638 |
DOI | 10.1109/CSSS.2011.5974827 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-06-27 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Training Algorithm design and analysis Viterbi algorithm Hidden Markov models Mouth Hidden expropriation Speech recognition HMM Visible state Cost accounting Lip reading |
Content Type | Text |
Resource Type | Article |
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