Dynamic language model for speech recognition

1995 ◽  
Vol 98 (2) ◽  
pp. 689-689
Author(s):  
Robert D. Strong
Keyword(s):  
Speech Recognition ◽  
Language Model ◽  
Dynamic Language

Topic identification techniques applied to dynamic language model adaptation for automatic speech recognition

2015 ◽  
Vol 42 (1) ◽  
pp. 101-112 ◽  
Author(s):  
J.D. Echeverry-Correa ◽  
J. Ferreiros-López ◽  
A. Coucheiro-Limeres ◽  
R. Córdoba ◽  
J.M. Montero
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Language Model ◽  
Model Adaptation ◽  
Topic Identification ◽  
Language Model Adaptation ◽  
Dynamic Language ◽  
Identification Techniques

scholarly journals Dynamic language model adaptation using presentation slides for lecture speech recognition

2007 ◽  
Author(s):  
Hiroki Yamazaki ◽  
Koji Iwano ◽  
Koichi Shinoda ◽  
Sadaoki Furui ◽  
Haruo Yokota
Keyword(s):  
Speech Recognition ◽  
Language Model ◽  
Model Adaptation ◽  
Language Model Adaptation ◽  
Dynamic Language

A dynamic language model for speech recognition

1991 ◽  
Author(s):  
F. Jelinek ◽  
B. Merialdo ◽  
S. Roukos ◽  
M. Strauss
Keyword(s):  
Speech Recognition ◽  
Language Model ◽  
Dynamic Language

scholarly journals Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition

2021 ◽  
Author(s):  
Zhong Meng ◽  
Sarangarajan Parthasarathy ◽  
Eric Sun ◽  
Yashesh Gaur ◽  
Naoyuki Kanda ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Language Model ◽  
Model Estimation ◽  
End To End

scholarly journals Phonetic Variation Modeling and a Language Model Adaptation for Korean English Code-Switching Speech Recognition

2021 ◽  
Vol 11 (6) ◽  
pp. 2866
Author(s):  
Damheo Lee ◽  
Donghyun Kim ◽  
Seung Yun ◽  
Sanghun Kim
Keyword(s):  
Speech Recognition ◽  
Language Model ◽  
Reduction Rate ◽  
Code Switching ◽  
Training Data ◽  
Target Domain ◽  
Phonetic Variation ◽  
Language Model Adaptation ◽  
Imbalanced Training Data ◽  
Lm Adaptation

In this paper, we propose a new method for code-switching (CS) automatic speech recognition (ASR) in Korean. First, the phonetic variations in English pronunciation spoken by Korean speakers should be considered. Thus, we tried to find a unified pronunciation model based on phonetic knowledge and deep learning. Second, we extracted the CS sentences semantically similar to the target domain and then applied the language model (LM) adaptation to solve the biased modeling toward Korean due to the imbalanced training data. In this experiment, training data were AI Hub (1033 h) in Korean and Librispeech (960 h) in English. As a result, when compared to the baseline, the proposed method improved the error reduction rate (ERR) by up to 11.6% with phonetic variant modeling and by 17.3% when semantically similar sentences were applied to the LM adaptation. If we considered only English words, the word correction rate improved up to 24.2% compared to that of the baseline. The proposed method seems to be very effective in CS 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

Transducer-based speech recognition with dynamic language models

2013 ◽  
Author(s):  
Munir Georges ◽  
Stephan Kanthak ◽  
Dietrich Klakow
Keyword(s):  
Speech Recognition ◽  
Language Models ◽  
Dynamic Language
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