Speaker identification system using empirical mode decomposition and an artificial neural network

Jian Da Wu, Yi Jang Tsai

Research output: Contribution to journalArticle

41 Citations (Scopus)

Abstract

This paper presents a speaker identification system using empirical mode decomposition (EMD) feature extraction method and artificial neural network in speaker identification. The EMD is an adaptive multi-resolution decomposition technique that appears to be suitable for non-linear, non-stationary data analysis. The EMD sifts the complex signal of time series without losing its original properties and then obtains some useful intrinsic mode function (IMF) components. Calculating the energy of each component can reduce the computation dimensions and enhance the performance of classification. The features were used as inputs to neural network classifiers for speaker identification. In the speaker identification, the back-propagation neural network (BPNN) and generalized regression neural network (GRNN) were applied to verify the performances and the training time in the proposed system. The experimental results indicated the GRNN can achieve better recognition rate performance with feature extraction using the EMD method than BPNN.

Original languageEnglish
Pages (from-to)6112-6117
Number of pages6
JournalExpert Systems with Applications
Volume38
Issue number5
DOIs
Publication statusPublished - 2011 May 1

Fingerprint

Identification (control systems)
Neural networks
Decomposition
Backpropagation
Feature extraction
Time series
Classifiers

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

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Speaker identification system using empirical mode decomposition and an artificial neural network. / Wu, Jian Da; Tsai, Yi Jang.

In: Expert Systems with Applications, Vol. 38, No. 5, 01.05.2011, p. 6112-6117.

Research output: Contribution to journalArticle

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