Empirical study of neural network language models for Arabic speech recognition

被引：28

作者：

Emami, Ahmad ^{[1
]}

Mangu, Lidia ^{[1
]}

机构：

[1] IBM Corp, Thomas J Watson Res Ctr, Yorktown Hts, NY 10598 USA

来源：

2007 IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING, VOLS 1 AND 2 | 2007年

关键词：

language modeling; speech recognition; neural networks;

D O I：

10.1109/ASRU.2007.4430100

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we investigate the use of neural network language models for Arabic speech recognition. By using a distributed representation of words, the neural network model allows for more robust generalization and is better able to fight the data sparseness problem. We investigate different configurations of the neural probabilistic model, experimenting with such parameters as N-gram order, output vocabulary, normalization method, and model size and parameters. Experiments were carried out on Arabic broadcast news and broadcast conversations data and the optimized neural network language models showed significant improvements over the baseline N-gram. model.

引用

页码：147 / 152

页数：6