Model based feature enhancement for automatic speech recognition in reverberant environments

2009 ◽  
Author(s):  
Alexander Krueger ◽  
Reinhold Haeb-Umbach
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Feature Enhancement ◽  
Model Based ◽  
Reverberant Environments

scholarly journals Phone based acoustic modeling for automatic speech recognition for Punjabi language

2021 ◽  
Vol 3 (1) ◽  
pp. 68-83
Author(s):  
Wiqas Ghai ◽  
Navdeep Singh
Keyword(s):  
Speech Recognition ◽  
Word Recognition ◽  
Automatic Speech Recognition ◽  
Recognition Accuracy ◽  
Model Based ◽  
The Media ◽  
Isolated Word ◽  
Whole Word ◽  
Speech Recognizer ◽  
Triphone Model

Punjabi language is a tonal language belonging to an Indo-Aryan language family and has a number of speakers all around the world. Punjabi language has gained acceptability in the media & communication and therefore deserves to have a place in the growing field of automatic speech recognition which has been explored already for a number of other Indian and foreign languages successfully. Some work has been done in the field of isolated word speech recognition for Punjabi language, but only using whole word based acoustic models. A phone based approach has yet to be applied for Punjabi language speech recognition. This paper describes an automatic speech recognizer that recognizes isolated word speech and connected word speech using a triphone based acoustic model on the HTK 3.4.1 speech Engine and compares the performance with acoustic whole word model based ASR system. Word recognition accuracy of isolated word speech was 92.05% for acoustic whole word model based system and 97.14% for acoustic triphone model based system whereas word recognition accuracy of connected word speech was 87.75% for acoustic whole word model based system and 91.62% for acoustic triphone model based system.

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