Speaker recognition by means of a combination of linear and nonlinear predictive models

03/07/2022

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by   Marcos Faundez-Zanuy, et al.

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This paper deals the combination of nonlinear predictive models with classical LPCC parameterization for speaker recognition. It is shown that the combination of both a measure defined over LPCC coefficients and a measure defined over predictive analysis residual signal gives rise to an improvement over the classical method that considers only the LPCC coefficients. If the residual signal is obtained from a linear prediction analysis, the improvement is 2.63 nonlinear predictive neural nets based model, the improvement is 3.68 efficient algorithm for reducing the computational burden is also proposed.