A statistical pattern recognition approach to robust recursive identification of non-stationary AR model of speech production system

We propose a robust recursive procedure based on a weighted recursive least squares (WRLS) algorithm with variable forgetting factor (VFF) and frame-based quadratic classifier for identification of nonstationary AR model of a speech production system. Also, two versions of the frame-based quadratic classifier design procedure, iterative quadratic classifications procedure (CIQC) and its real-time modification (RTQC), are considered. A comparative experimental analysis is done according to the results obtained in analyzing speech signal with voiced and mixed excitation segments. Experimental results justify that two main problems of LPC speech analysis, nonstationarity of LPC parameters and non-appropriateness of AR modeling of speech (particularly on the voiced frames), can be solved by application of the proposed robust procedure. As for the comparison of CIQC and RTQC algorithm, it has been observed that superior results are obtained by using the proposed method with the RTQC algorithm and it is recommended for use in the nonstationary AR speech model identification.