ARMA lattice modeling for isolated word speech recognition

被引：0
|
作者：
Kwan, HK [1 ]
Li, TX [1 ]
机构：
[1] Univ Windsor, Fac Engn, Windsor, ON N9B 3P4, Canada
来源：
PROCEEDINGS OF THE 43RD IEEE MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS I-III | 2000年
关键词：
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we introduce an auto-regressive moving average (ARMA) lattice model for speech modeling. The speech characteristics are modeled and expressed in the form of lattice reflection coefficients for classification. Self-Organization Map (SOM) is used to build codebooks for classification and recognition of the lattice reflection coefficients. Experimental results based on an isolated word speech database of 10 words/names indicate that the ARMA lattice model achieves superior recognition performance as compared to those of the conventional auto-regressive (AR) model.
引用
收藏
页码：1186 / 1190
页数：5
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