An on-line adaptive neural network for speech recognition

被引：0
|
作者：
Zhang L.-P. [1 ]
Li L.M. [2 ]
Chi Z. [1 ]
机构：
[1] Department of Electronic Engineering, Hong Kong Polytechnic University
[2] School of Business Administration, National University of Singapore
来源：
International Journal of Speech Technology | 1998年 / 2卷 / 3期
关键词：
Accumulative learning; Adaptive neural networks; Dynamic recognition neural network (DRNN); Hidden markov model; Speech recognition;
D O I：
10.1007/BF02111211
中图分类号：
学科分类号：
摘要：
In this paper, we present an on-line learning neural network model, Dynamic Recognition Neural Network (DRNN), for real-time speech recognition. The property of accumulative learning of the DRNN makes it very suitable for real-time speech recognition with on-line learning. A comparison between the DRNN and Hidden Markov Model (HMM) shows that the computational complexity of the former is lower than that of the latter in both training and recognition. Encouraging results are obtained when the DRNN is tested on a BUPT digit database (Mandarin) and on the on-line learning of twenty isolated English computer command words. © 1998 Kluwer Academic Publishers.
引用
收藏
页码：241 / 248
页数：7
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