Acoustic training system for speaker independent continuous arabic speech recognition system

被引：4
|
作者：
Nofal, M [1 ]
Abdel-Raheem, E [1 ]
El Henawy, H [1 ]
Kader, NA [1 ]
机构：
[1] Ain Shams Univ, Cairo, Egypt
来源：
Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology | 2004年
关键词：
speech processing; language processing;
D O I：
10.1109/ISSPIT.2004.1433721
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The paper presents an acoustic training system for building acoustic models for a medium vocabulary speaker independent continuous speech recognition system. A speech database is constructed to train the acoustic models. The acoustic models are constructed, and trained. A test set database is constructed to test the accuracy of the acoustic models. Also 4 language models of two main types: bigram and context free grammar, were built and used in tests. Our results show 5.26 % and 2.72 % word error rates for 1340 and 306 words bigram based language models, respectively. Our results show also 0.19 % and 0.99% word error rates for 1340 and 306 words context free grammar based language models, respectively.
引用
收藏
页码：200 / 203
页数：4
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