An information set-based robust text-independent speaker authentication

Jeevan Medikonda, Saurabh Bhardwaj, Hanmandlu Madasu

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a method for the extraction of twofold information set (TFIS) features for the text-independent speaker recognition. The method takes the Mel frequency cepstral coefficients from the frames of a sample speech signal and forms a matrix. From this, both spatial and temporal information components are derived based on the information set concept using the entropy framework. The TFIS features comprising their combination of two components are less in number thus reducing the computational time, complexity and improving the performance under the noisy environment. The proposed approach is tested on three datasets namely NIST-2003, VoxForge 2014 speech corpus and VCTK speech corpus in terms of speed, computational complexity, memory requirement and accuracy. Its performance is validated under different noisy environments at different signal-to-noise ratios.

Original languageEnglish
Pages (from-to)5271-5287
Number of pages17
JournalSoft Computing
Volume24
Issue number7
DOIs
Publication statusPublished - 01-04-2020

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

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