scholarly journals Real-time speech recognition engine for accent correction using Hidden Markov Model

2018 ◽  
Author(s):  
J. B. Lazaro ◽  
M. C. P. Po ◽  
L. M. Ramones ◽  
P. M. L. Tolidanes
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Real Time ◽  
Hidden Markov ◽  
Speech Recognition Engine

Speech Silicon: An FPGA Architecture for Real-Time Hidden Markov-Model-Based Speech Recognition

2006 ◽  
Vol 2006 (1) ◽  
pp. 048085 ◽  
Author(s):  
Jeffrey Schuster ◽  
Kshitij Gupta ◽  
Raymond Hoare ◽  
AlexK Jones
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Real Time ◽  
Hidden Markov ◽  
Model Based ◽  
Fpga Architecture

Speech Silicon: An FPGA Architecture for Real-Time Hidden Markov-Model-Based Speech Recognition

2006 ◽  
Vol 2006 ◽  
pp. 1-19 ◽  
Author(s):  
Jeffrey Schuster ◽  
Kshitij Gupta ◽  
Raymond Hoare ◽  
Alex K. Jones
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Real Time ◽  
Hidden Markov ◽  
Model Based ◽  
Fpga Architecture

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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