Open AccessWord recognition from speech signal using linear predictive coding and spectrum analysis
Author(s) -
Mandeep Singh,
Gurpreet Singh
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.13285
Subject(s) - speech recognition , linear predictive coding , computer science , word (group theory) , linear prediction , speech coding , pattern recognition (psychology) , word recognition , signal (programming language) , artificial intelligence , voice activity detection , coding (social sciences) , speech processing , mathematics , statistics , linguistics , philosophy , geometry , reading (process) , programming language
This paper presents a technique for isolated word recognition from speech signal using Spectrum Analysis and Linear Predictive Coding (LPC). In the present study, only those words have been analyzed which are commonly used during a telephonic conversations by criminals. Since each word is characterized by unique frequency spectrum signature, thus, spectrum analysis of a speech signal has been done using certain statistical parameters. These parameters help in recognizing a particular word from a speech signal, as there is a unique value of a feature for each word, which helps in distinguishing one word from the other. Second method used is based on LPC coefficients. Analysis of features extracted using LPC coefficients help in identification of a specific word from the input speech signal. Finally, a combination of best features from these two methods has been used and a hybrid technique is proposed. An accuracy of 94% has been achieved for sample size of 400 speech words.