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“A speech recognizer” a tool to recognize the high clarity speech signal based on existing speech using ISCA

  • M. Navaneetha VelammalEmail author
  • P. Nirmal Kumar
Article
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Abstract

In this research a new way introduced for solving the underdetermined blind speech signal separation problem when the number of observation is less than the sources for which the ICA is no longer applicable, which enhance the time complexity for separation of signal. To resolve that, Improved Sparse Component Analysis (ISCA) is introduced to exploit the sparse nature of TF domain, which adopt a two-step processing that contains mixing matrix estimation followed by separation of source. This ISCA is based on fuzzy c-means with Particle swarm optimization (PSO) algorithm for mixed matrix Estimation. In our work PSO is used to separate the accurate voice signal from the random mixed signal by finding the best optima solution in the cluster part. Then the source signal separation is carried out based on the shortest path. These initial processing is done and verified by Mat lab and hardware description language is generated using HDL coder and it is synthesized using Xilinx ISE. The final result illustrates that the proposed system has an improved performance in terms of SNR, Efficiency and Accuracy.

Keywords

Underdetermined speech separation Improved fuzzy c-means with PSO algorithm Hardware description language Real time speech separation 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Electronics and Communication Engineering DepartmentFrancis Xavier Engineering CollegeTirunelveliIndia
  2. 2.Electronics and Communication EngineeringAnna UniversityChennaiIndia

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