Unalike Methodologies of Feature Extraction & Feature Matching in Speech Recognition

被引：0
|
作者：
Tripathy, Ruchismita [1 ]
Tripathy, Hrudaya Kumar [1 ]
机构：
[1] KIIT Univ, Sch Comp Engn, Bhubaneswar, Odisha, India
来源：
2014 INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND APPLICATIONS (ICHPCA) | 2014年
关键词：
Automatic Speech Recognition; Feature Extraction and Matching Techniques; Graphical Representation on the Performance Metrics;
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 present scenario, the application of speech science has a vital role to produce the biometric applications. After so many research and improvement of Automatic Speech Recognition, accuracy of speech recognition is one of the challenging task. Various feature extraction techniques like Linear Predictive Coding, cepstral analysis, Local Discriminant Base, Restricted Boltzmann Machines have been discussed since past days. Similarly, a lot of debates have been arranged among the researchers for feature matching. Some of them are Hidden Markov Model (HMM), Dynamic time warping (DTW), Deep Belief Network. This paper is a clear reflection of automatic speech recognition. It describes various feature extraction and matching and focuses on analytical study based on performance metrics like Word Error Rate (WER) and accuracy of these techniques.
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页数：6
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