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

arXiv: https://arxiv.org/abs/2203.03190 · HTML: https://mlsanity.com/paper/2203.03190/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 7 Mar 2022.

## Abstract (arXiv)

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% (error rate drops from 6.31% to 3.68%) and if it is computed through a nonlinear predictive neural nets based model, the improvement is 3.68%. An efficient algorithm for reducing the computational burden is also proposed.