scholarly journals Detection of Respiratory Effort-Related Arousals Using a Hidden Markov Model and Random Decision Forest

Author(s):  
János Szalma ◽  
András Bánhalmi ◽  
Vilmos Bilicki
Author(s):  
Jinglian Liang ◽  
Chao Xu ◽  
Zhiyong Feng ◽  
Xirong Ma

Facial expressions can be mainly conveyed by only a few discriminative facial regions of interest. In this paper, we study the discriminative regions for facial expression recognition from video sequences. The goal of our method is to explore and make use of the discriminative regions for different facial expressions. For this purpose, we propose a Hidden Markov Model (HMM) Decision Forest (HMMDF). In this framework, each tree node is a discriminative classifier, which is constructed by combining weighted HMMs. Motivated by a psychological theory of "elimination by aspects", several HMMs on each node are modeled respectively for facial regions, which have discriminative capabilities for classification. The weights for these HMMs can be further adjusted according to the contributions of facial regions. Extensive experiments validate the effectiveness of discriminative regions on facial expression, and the experimental results show that the proposed HMMDF framework yields dramatic improvements in facial expression recognition compared to existing methods.


2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  

2016 ◽  
Vol 7 (2) ◽  
pp. 76-82
Author(s):  
Hugeng Hugeng ◽  
Edbert Hansel

We have built an application of speech recognition for Indonesian geography dictionary based on Android operating system, named GAIA. This application uses a smartphone as a device to receive input in the form of a spoken word from a user. The approach used in recognition is Hidden Markov Model which is contained in the Pocketsphinx library. The phonemes used are Indonesian phonemes’ rule. The advantage of this application is that it can be used without internet access. In the application testing, word detection is done with four conditions to determine the level of accuracy. The four conditions are near silent, near noisy, far silent, and far noisy. From the testing and analysis conducted, it can be concluded that GAIA application can be built as a speech recognition application on Android for Indonesian geography dictionary; with the results in the near silent condition accuracy of word recognition reaches an average of 52.87%, in the near noisy reaches an average of 14.5%, in the far silent condition reaches an average of 23.2%, and in the far noisy condition reaches an average of 2.8%. Index Terms—speech recognition, Indonesian geography dictionary, Hidden Markov Model, Pocketsphinx, Android.


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