Open AccessSpeech Recognition using Cross Correlation Algorithm Intended for Noise Reduction
Author(s) -
Gagandeep Kaur,
Seema Baghla
Publication year - 2018
Publication title -
asian journal of computer science and technology
Language(s) - English
Resource type - Journals
eISSN - 2583-7907
pISSN - 2249-0701
DOI - 10.51983/ajcst-2018.7.3.1899
Subject(s) - computer science , speech recognition , microphone , vocabulary , noise (video) , audio mining , speech analytics , domain (mathematical analysis) , software , natural language processing , speech processing , artificial intelligence , database , voice activity detection , telecommunications , mathematical analysis , philosophy , linguistics , mathematics , sound pressure , programming language , image (mathematics)
Biometrics is presently a buzzword in the domain of information security as it provides high degree of accuracy in identifying an individual. Speech recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to a machine-readable format. Rudimentary speech recognition software has a limited vocabulary of words and phrases, and it may only identify these if they are spoken very clearly. The research work is intended to build a GUI environment which would provide provisions to record the speech and would assist in multiplying the database. The research work is primarily focused to implement a system capable of recognizing a user’s speech and creating audio files that can be added up to create a dynamic template or database. The research work emphasizes on directly recording the spoken words avoiding the problems with use of microphone. On appropriate recording and removal of the noise, the best matched audio file from the template is recognized when an input is provided externally on the basis of graphs created by considering correlation.