Hidden Markov Model of Information System with Component-Wise Storage Devices

2019 ◽  
pp. 354-364
Author(s):  
Yuriy E. Obzherin ◽  
Stanislav M. Sidorov ◽  
Mikhail M. Nikitin
Keyword(s):  
Information System ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Storage Devices

scholarly journals Arrival Time Estimation for Bus Information System Using Hidden Markov Model

2017 ◽  
Vol 6 (4) ◽  
pp. 189-196
Author(s):  
Chul Young Park ◽  
Hong Geun Kim ◽  
Chang Sun Shin ◽  
Yong Yun Cho ◽  
Jang Woo Park
Keyword(s):  
Information System ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Arrival Time ◽  
Hidden Markov ◽  
Time Estimation

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.

Hidden Markov Model Classification of Myoelectric Signals in Speech

2001 ◽  
Author(s):  
A. D. Chan ◽  
K. Englehart ◽  
B. Hudgins ◽  
D. F. Lovely
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Myoelectric Signals ◽  
Model Classification
Sign in / Sign up

Export Citation Format

Share Document