Feature Vector Selection of Fusion of MFCC and SMRT Coefficients for SVM Classifier Based Speech Recognition System

被引：0
|
作者：
Mini, P. P. [1 ]
Thomas, Tessamma [1 ]
Gopikakumari, R. [2 ]
机构：
[1] CUSAT, Dept Elect, Kochi, Kerala, India
[2] CUSAT, Sch Engn, Kochi, Kerala, India
来源：
PROCEEDINGS OF THE 2018 8TH INTERNATIONAL SYMPOSIUM ON EMBEDDED COMPUTING AND SYSTEM DESIGN (ISED 2018) | 2018年
关键词：
Speech recognition; SMRT; MFCC; SVM; SPEAKER IDENTIFICATION;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Automatic speech recognition system involves two phases namely training and matching. Features extracted from known speech signals are used to train the system and is matched with the features generated from unknown speech. Selection of good technique for feature extraction and feature matching has a prominent effect in speech recognition. Feature vector selection, from Mel Frequency Cepstral Coefficients (MFCC) and Sequency based Mapped Real Transform (SMRT) coefficients, is presented in this work. Linear kernel based Support Vector Machine (SVM) is used as the classifier for recognizing the speech. The results show that fusion of transform coefficients increases the recognition rate and also the efficiency of the proposed system.
引用
收藏
页码：153 / 157
页数：5
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