Modelling intonational phrase structure with artificial neural networks
Grażyna Demenko, Wiktor Jassem · 1999
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.