Research on Mongolian Acoustic Model Based on the Triphone DDBHMM

2012 ◽  
Vol 239-240 ◽  
pp. 1100-1103 ◽  
Author(s):  
Jing Yun ◽  
Zhi Qiang Ma ◽  
Yi La Su ◽  
Xiu Lan Xie
Keyword(s):  
Speech Recognition ◽  
Recognition Performance ◽  
Experimental Results ◽  
Acoustic Model ◽  
Continuous Speech ◽  
Continuous Speech Recognition ◽  
Model Based ◽  
Duration Distribution

Triphone DDBHMM (Duration Distribution Based HMM) is presented as the acoustic model for Mongolian continuous speech recognition, and the Mongolian Acoustic Model is optimized by state-binding. The experiment made a comparison of the triphone DDBHMM, diphone DDBHMM, triphone HMM on HTK platform and analyzed their effects on the accuracy of acoustic layer. The experimental results have showed that Triphone DDBHMM significantly improves the recognition performance of continuous speech recognition in Mongolian.

Demonstrated real‐time continuous speech recognition performance

1980 ◽  
Vol 67 (S1) ◽  
pp. S15-S15
Author(s):  
Larry Bahler ◽  
Steve Moshier ◽  
Peter Brown ◽  
James Baker
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Recognition Performance ◽  
Continuous Speech ◽  
Continuous Speech Recognition

scholarly journals Building Acoustic and Language Model for Continuous Speech Recognition in Bahasa Indonesia

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Vincent Elbert Budiman ◽  
Andreas Widjaja
Keyword(s):  
Speech Recognition ◽  
Error Rate ◽  
Language Model ◽  
Beam Width ◽  
Acoustic Model ◽  
Continuous Speech ◽  
Continuous Speech Recognition ◽  
Word Error Rate ◽  
Testing Data ◽  
Bahasa Indonesia

Here a development of an Acoustic and Language Model is presented. Low Word Error Rate is an early good sign of a good Language and Acoustic Model. Although there are still parameters other than Words Error Rate, our work focused on building Bahasa Indonesia with approximately 2000 common words and achieved the minimum threshold of 25% Word Error Rate. There were several experiments consist of different cases, training data, and testing data with Word Error Rate and Testing Ratio as the main comparison. The language and acoustic model were built using Sphinx4 from Carnegie Mellon University using Hidden Markov Model for the acoustic model and ARPA Model for the language model. The models configurations, which are Beam Width and Force Alignment, directly correlates with Word Error Rate. The configurations were set to 1e-80 for Beam Width and 1e-60 for Force Alignment to prevent underfitting or overfitting of the acoustic model. The goals of this research are to build continuous speech recognition in Bahasa Indonesia which has low Word Error Rate and to determine the optimum numbers of training and testing data which minimize the Word Error Rate.  

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