A hidden Markov model based visual speech synthesizer

Author(s):  
J.J. Williams ◽  
A.K. Katsaggelos ◽  
M.A. Randolph
2009 ◽  
pp. 356-387
Author(s):  
Say Wei Foo ◽  
Liang Donga

The basic building blocks of visual speech are the visemes. Unlike phonemes, the visemes are, however, confusable and easily distorted by the contexts in which they appear. Classifiers capable of distinguishing the minute difference among the different categories are desirable. In this chapter, we describe two Hidden Markov Model based techniques using the discriminative approach to increase the accuracy of visual speech recognition. The approaches investigated include Maximum Separable Distance (MSD) training strategy (Dong, 2005) and Two-channel training approach (Dong, 2005; Foo, 2003; Foo, 2002) The MSD training strategy and the Two-channel training approach adopt a proposed criterion function called separable distance to improve the discriminative power of an HMM. The methods are applied to identify confusable visemes. Experimental results indicate that higher recognition accuracy can be attained using these approaches than that using conventional HMM.


Bioacoustics ◽  
2019 ◽  
Vol 29 (2) ◽  
pp. 140-167 ◽  
Author(s):  
Susannah J. Buchan ◽  
Rodrigo Mahú ◽  
Jorge Wuth ◽  
Naysa Balcazar-Cabrera ◽  
Laura Gutierrez ◽  
...  

2016 ◽  
Vol 23 (19) ◽  
pp. 3175-3195 ◽  
Author(s):  
Ayan Sadhu ◽  
Guru Prakash ◽  
Sriram Narasimhan

A robust hybrid hidden Markov model-based fault detection method is proposed to perform multi-state fault classification of rotating components. The approach presented in this paper enhances the performance of the standard hidden Markov model (HMM) for fault detection by performing a series of pre-processing steps. First, the de-noised time-scale signatures are extracted using wavelet packet decomposition of the vibration data. Subsequently, the Teager Kaiser energy operator is employed to demodulate the time-scale components of the raw vibration signatures, following which the condition indicators are calculated. Out of several possible condition indicators, only relevant features are selected using a decision tree. This pre-processing improves the sensitivity of condition indicators under multiple faults. A Gaussian mixing model-based hidden Markov model (HMM) is then employed for fault detection. The proposed hybrid HMM is an improvement over traditional HMM in that it achieves better separation of the feature space leading to more robust state estimation under multiple fault states and measurement noise scenarios. A simulation employing modulated signals and two experimental validation studies are presented to demonstrate the performance of the proposed method.


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