Text dependent voice recognition system using MFCC and VQ for security applications

Ashwin Nair Anil Kumar, Senthil Arumugam Muthukumaraswamy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


This paper presents the implementation of a practical voice recognition system using MATLAB (R2014b) to secure a given user's system so that only the user may access it. Voice recognition systems have two phases, training and testing. During the training phase, the characteristic features of the speaker are extracted from the speech signal and stored in a database. In the testing phase, the stored audio features of the test voice sample are compared with the voice samples in the database and determined if a match exists. For this research, Mel Frequency Cepstral Coefficients (MFCCs) were chosen to represent the feature vectors of the user's voice as it accurately simulates the behavior of the human ear. This characteristic of the MFCCs makes them an excellent measure of speaker characteristics. The feature matching process is then performed by subjecting the MFCCs to vector quantization using the LBG (Linde-Buzo-Gray) algorithm. In practical scenarios, noise is a major factor that adversely influences a voice recognition system. The paper addresses this issue by utilizing spectral subtraction to remove environmental noise affecting the speech signal thereby increasing the robustness of the system.

Original languageEnglish
Title of host publication2017 International conference of Electronics, Communication and Aerospace Technology (ICECA)
Number of pages7
ISBN (Electronic)9781509056866
ISBN (Print)9781509056859
Publication statusPublished - 18 Dec 2017


  • Mel Frequency Cepstral Coefficients (MFCCs)
  • Speaker Identification
  • Spectral Subtraction
  • Vector Quantization (VQ)
  • Voice Recognition

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Instrumentation
  • Safety, Risk, Reliability and Quality


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