Voice Recognition Application by Using Fisher's Linear Discriminant Analysis (FLDA) Feature Extraction

被引：0

作者：

Rachmad, Aeri ^{[1
]}

Anamisa, Devie Rosa ^{[1
]}

Bintari, Novia Putri ^{[1
]}

机构：

[1] Univ Trunojoyo Madura, Fac Engn, Jl Raya Telang, Kamal 69162, Bangkalan, Indonesia

来源：

ADVANCED SCIENCE LETTERS | 2017年 / 23卷 / 12期

关键词：

Speech Recognition; Fisher Linear Discriminant Analysis (FLDA); Pattern; Feature Extraction;

D O I：

10.1166/asl.2017.10636

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

In classifying the pattern, the number of learning data used is often very limited, but the number of dimensions is very high. Fisher linear discriminant analysis (FLDA) is a pattern classification method that is widely used in pattern recognition feature extraction and reduction of linear dimensions. FLDA method is able to analyze the data and study the relationship between a set of categorical predictors and response for pattern recognition applications, including speech pattern recognition is used as a command to the system in the presence of employees of the agency. FLDA has the ability to distinguish one pattern with another pattern so that the pattern does not belong to the other so that the pattern of this match only one sound input with voice database which has resulted in data that is best suited for people with a sound level of accuracy that reaches 53.3% for opportunities best. This shows that this method is good enough to be used in the process of the speech recognition.

引用

页码：12344 / 12348

页数：5

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