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

2018 ◽  
Author(s):  
János Szalma ◽  
András Bánhalmi ◽  
Vilmos Bilicki
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Respiratory Effort ◽  
Random Decision Forest ◽  
Decision Forest

Hidden Markov Model Decision Forest for Dynamic Facial Expression Recognition

2015 ◽  
Vol 29 (07) ◽  
pp. 1556010 ◽  
Author(s):  
Jinglian Liang ◽  
Chao Xu ◽  
Zhiyong Feng ◽  
Xirong Ma
Keyword(s):  
Facial Expression ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Hidden Markov ◽  
Psychological Theory ◽  
Expression Recognition ◽  
Elimination By Aspects ◽  
Decision Forest

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.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions

Implementation of Android Based Speech Recognition for Indonesian Geography Dictionary

2016 ◽  
Vol 7 (2) ◽  
pp. 76-82
Author(s):  
Hugeng Hugeng ◽  
Edbert Hansel
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Spoken Word ◽  
Silent Condition ◽  
Word Detection ◽  
Index Terms ◽  
To Receive ◽  
Noisy Condition

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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