Quadratic classifier with sliding training data set in robust recursive identification of non-stationary AR model of speech

In this work, a robust recursive procedure based on WRLS algorithm with VFF and a quadratic classifier with sliding training data set for identification of non-stationary AR model of speech production system is proposed. Experimental analysis is done according to the results obtained in analyzing speech signal with voiced and mixed excitation segments. Presented experimental results justify that two main problems of LPC speech analysis, non-stationarity of LPC parameters and non-appropriateness of AR modeling of speech (particularly on the voiced frames), can be solved by using the proposed robust procedure.