Error correction of automatic speech recognition based on normalized web distance

2014 ◽  
Author(s):  
E. Byambakhishig ◽  
K. Tanaka ◽  
Ryo Aihara ◽  
Toru Nakashika ◽  
Tetsuya Takiguchi ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Error Correction ◽  
Automatic Speech Recognition

scholarly journals FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition

2021 ◽  
Author(s):  
Yichong Leng ◽  
Xu Tan ◽  
Rui Wang ◽  
Linchen Zhu ◽  
Jin Xu ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Error Correction ◽  
Automatic Speech Recognition

scholarly journals Location-Based End-to-End Speech Recognition with Multiple Language Models

2019 ◽  
Vol 33 ◽  
pp. 9975-9976
Author(s):  
Zhijie Lin ◽  
Kaiyang Lin ◽  
Shiling Chen ◽  
Linlin Li ◽  
Zhou Zhao
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Error Correction ◽  
Automatic Speech Recognition ◽  
Language Model ◽  
Language Models ◽  
Learning Approaches ◽  
Semantic Error ◽  
End To End

End-to-End deep learning approaches for Automatic Speech Recognition (ASR) has been a new trend. In those approaches, starting active in many areas, language model can be considered as an important and effective method for semantic error correction. Many existing systems use one language model. In this paper, however, multiple language models (LMs) are applied into decoding. One LM is used for selecting appropriate answers and others, considering both context and grammar, for further decision. Experiment on a general location-based dataset show the effectiveness of our method.

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