Maximum entropy direct model as a unified model for acoustic modeling in speech recognition

2004 ◽  
Author(s):  
Hong-Kwang Jeff Kuo ◽  
Yuqing Gao
Keyword(s):  
Speech Recognition ◽  
Maximum Entropy ◽  
Unified Model ◽  
Acoustic Modeling ◽  
Direct Model

Improved acoustic modeling for continuous speech recognition

1990 ◽  
Author(s):  
C.-H. Lee ◽  
E. Giachin ◽  
L. R. Rabiner ◽  
R. Pieraccini ◽  
A. E. Rosenberg
Keyword(s):  
Speech Recognition ◽  
Acoustic Modeling ◽  
Continuous Speech ◽  
Continuous Speech Recognition

Discriminative maximum entropy language model for speech recognition

2005 ◽  
Author(s):  
Chuang-Hua Chueh ◽  
To-Chang Chien ◽  
Jen-Tzung Chien
Keyword(s):  
Speech Recognition ◽  
Maximum Entropy ◽  
Language Model

scholarly journals Selection of acoustic modeling unit for Tibetan speech recognition based on deep learning

2021 ◽  
Vol 336 ◽  
pp. 06014
Author(s):  
Baojia Gong ◽  
Rangzhuoma Cai ◽  
Zhijie Cai ◽  
Yuntao Ding ◽  
Maozhaxi Peng
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Experimental Verification ◽  
Character Segmentation ◽  
Acoustic Modeling ◽  
Overall Performance ◽  
Primary Problem ◽  
Selection Of

The selection of the speech recognition modeling unit is the primary problem of acoustic modeling in speech recognition, and different acoustic modeling units will directly affect the overall performance of speech recognition. This paper designs the Tibetan character segmentation and labeling model and algorithm flow for the purpose of solving the problem of selecting the acoustic modeling unit in Tibetan speech recognition by studying and analyzing the deficiencies of the existing acoustic modeling units in Tibetan speech recognition. After experimental verification, the Tibetan character segmentation and labeling model and algorithm achieved good performance of character segmentation and labeling, and the accuracy of Tibetan character segmentation and labeling reached 99.98%, respectively.

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