On a Hybrid NN/HMM Speech Recognition System with a RNN-Based Language Model

2014 ◽  
pp. 315-321 ◽  
Author(s):  
Daniel Soutner ◽  
Jan Zelinka ◽  
Luděk Müller
Keyword(s):  
Speech Recognition ◽  
Language Model ◽  
Recognition System ◽  
Speech Recognition System

An Improved Speech Recognition System Based on Transformer Language Model

2021 ◽  
Author(s):  
Qi Yue ◽  
Weiliang Shi ◽  
Yi He ◽  
Jing Chu ◽  
Zhan Han ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Language Model ◽  
Recognition System ◽  
Speech Recognition System

Arabic Phonetic Dictionaries for Speech Recognition

2009 ◽  
Vol 2 (4) ◽  
pp. 67-80 ◽  
Author(s):  
Mohamed Ali ◽  
Moustafa Elshafei ◽  
Mansour Al-Ghamdi ◽  
Husni Al-Muhtaseb
Keyword(s):  
Speech Recognition ◽  
Language Model ◽  
Recognition System ◽  
Speech Recognition System ◽  
Broadcast News ◽  
Word Error Rate ◽  
Large Vocabulary ◽  
Essential Components ◽  
News Corpus ◽  
Arabic Speech Recognition

Phonetic dictionaries are essential components of large-vocabulary speaker-independent speech recognition systems. This paper presents a rule-based technique to generate phonetic dictionaries for a large vocabulary Arabic speech recognition system. The system used conventional Arabic pronunciation rules, common pronunciation rules of Modern Standard Arabic, as well as some common dialectal cases. The paper gives in detail an explanation of these rules as well as their formal mathematical presentation. The rules were used to generate a dictionary for a 5.4 hour corpus of broadcast news. The rules and the phone set were tested and evaluated on an Arabic speech recognition system. The system was trained on 4.3 hours of the 5.4 hours of Arabic broadcast news corpus and tested on the remaining 1.1 hours. The phonetic dictionary contains 23,841 definitions corresponding to about 14232 words. The language model contains both bi-grams and tri-grams. The Word Error Rate (WER) came to 9.0%.

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