Speech Recognition Using MATLAB and Cross-Correlation Technique

Ledisi Giok Kabari and Marcus B. Chigoziri
Additional contact information
Ledisi Giok Kabari: Ken Saro-Wiwa Polytechnic, Bori, Nigeria
Marcus B. Chigoziri: Ignatius Ajuru University of Education, Port Harcourt, Nigeria.

European Journal of Engineering and Technology Research, 2019, vol. 4, issue 8, 1-3

Abstract: Speech is a prominent communication method among humans, whereas the communication between human and computers were based on text user interface and graphic user interface. Speech recognition is used in almost every security project where you need to speak and tell your password to computer and is also used for automation. This paper demonstrates a model that enhances technological advancement where humans and computers interact via voice user interface. In developing the model, cross correlation was implemented in MATLAB to compare two or more signals and detect the most accurate one of the all. We are actually used cross correlation to find similarity between our recorded Signal files and the testing signal. Thus we were able to develop a model where machines can differentiate between commands and act upon them.

Keywords: Cross Correlation; MATLAB; Recorded Signal; Signal Processing; Speech Recognition; Testing Signal (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eu-opensci.org/index.php/ejeng/article/view/61437 Abstract page (text/html)
https://eu-opensci.org/index.php/ejeng/article/download/61437/12189 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:4:y:2019:i:8:id:61437

DOI: 10.24018/ejeng.2019.4.8.1437

Access Statistics for this article

More articles in European Journal of Engineering and Technology Research from European Open Science
Bibliographic data for series maintained by Support ().