Improving N-gram language modeling for code-switching speech recognition

2017 ◽  
Author(s):  
Zhiping Zeng ◽  
Haihua Xu ◽  
Tze Yuang Chong ◽  
Eng-Siong Chng ◽  
Haizhou Li
Keyword(s):  
Speech Recognition ◽  
Language Modeling ◽  
Code Switching ◽  
N Gram

Using morphemes in language modeling and automatic speech recognition of Amharic

2012 ◽  
Vol 20 (2) ◽  
pp. 235-259 ◽  
Author(s):  
MARTHA YIFIRU TACHBELIE ◽  
SOLOMON TEFERRA ABATE ◽  
WOLFGANG MENZEL
Keyword(s):  
Speech Recognition ◽  
Substantial Reduction ◽  
Recognition Task ◽  
Recognition System ◽  
Language Modeling ◽  
Language Models ◽  
Vocabulary Size ◽  
Severe Problem ◽  
Morphologically Rich Languages ◽  
N Gram

AbstractThis paper presents morpheme-based language models developed for Amharic (a morphologically rich Semitic language) and their application to a speech recognition task. A substantial reduction in the out of vocabulary rate has been observed as a result of using subwords or morphemes. Thus a severe problem of morphologically rich languages has been addressed. Moreover, lower perplexity values have been obtained with morpheme-based language models than with word-based models. However, when comparing the quality based on the probability assigned to the test sets, word-based models seem to fare better. We have studied the utility of morpheme-based language models in speech recognition systems and found that the performance of a relatively small vocabulary (5k) speech recognition system improved significantly as a result of using morphemes as language modeling and dictionary units. However, as the size of the vocabulary increases (20k or more) the morpheme-based systems suffer from acoustic confusability and did not achieve a significant improvement over a word-based system with an equivalent vocabulary size even with the use of higher order (quadrogram) n-gram language models.

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.

scholarly journals Profiling household appliance electricity usage with N-gram language modeling

2016 ◽  
Author(s):  
Daoyuan Li ◽  
Tegawende F. Bissyande ◽  
Sylvain Kubler ◽  
Jacques Klein ◽  
Yves Le Traon
Keyword(s):  
Language Modeling ◽  
Household Appliance ◽  
N Gram

Written-domain language modeling for automatic speech recognition

2013 ◽  
Author(s):  
Haşim Sak ◽  
Yun-hsuan Sung ◽  
Françoise Beaufays ◽  
Cyril Allauzen
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Language Modeling

Morphology-based language modeling for arabic speech recognition

2004 ◽  
Author(s):  
Dimitra Vergyri ◽  
Katrin Kirchhoff ◽  
Kevin Duh ◽  
Andreas Stolcke
Keyword(s):  
Speech Recognition ◽  
Language Modeling ◽  
Arabic Speech Recognition

scholarly journals Statistical Language Modeling for Automatic Speech Recognition of Agglutinative Languages

10.5772/6380 ◽  
2008 ◽  
Author(s):  
Ebru Arsoy ◽  
Mikko Kurimo ◽  
Murat Saralar ◽  
Teemu Hirsimki ◽  
Janne Pylkknen ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Language Modeling ◽  
Statistical Language Modeling ◽  
Agglutinative Languages
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