Parametric representation of speech employing multi-component AFM signal model

Abstract

In this paper, we have proposed parametric representation of speech signals employing a novel multi-component amplitude and frequency modulated (AFM) sinusoidal signal model. The Fourier–Bessel (FB) series expansion is used to separate the multi-component speech signal into a set of mono-component signals. It has been shown that the first component or low-frequency component can be modeled with one set of parameters for the complete signal length. For other components of the speech which is a non-stationary signal, segmentation is required in order to apply the AFM signal model. We have proposed modeling of the second and third (and higher) components based on the AFM model with time-varying parameters. Thus, the signal is to be modeled in segments by selecting suitable length where the AFM signal model is admissible. The Itakura–Saito distance and root mean square log-spectral measure have been applied to determine distortion between the actual and modeled speech signals. Simulation results demonstrate the suitability of the AFM signal model for speech signal representation.