An Adaptive Signal Processing System for Voiced Speech with Automatic Spectrum Selector

In this paper, adaptive spectrum analysis algorithms for voiced speech are discussed. A new adaptive signal processing system which uses a modified MIS algorithm and has a spectrum selector using a neural network is proposed. Based on the properties of voiced speech, we first test an identification algorithm which estimates AR parameters from the first moment of the observed signal and investigate its sensitivity to noise. In order to compare the modified MIS for voiced speech analysis with the above first moment analysis, the frequency domain properties of weighting factor A which is used in the modified MIS are presented. This paper shows that we should select only the accurate spectra from among the results given by the modified MIS. The selection of spectra is automatically performed using a neural network. Using above methods, we construct a new analysis system for voiced speech. Experiments on real speech show that the proposed system is effective for speech analysis.