Incorporating dynamic track information for all-pole parameter estimation in noise

In this paper, we present a novel post-processing scheme to improve autoregressive (AR) speech parameter estimation in noise. The proposed technique exploits temporal correlation between dynamic all-pole parameters to capture natural speech evolution. To achieve this, a Kalman tracking scheme is proposed to track line spectrum frequency (LSF) trajectories with system parameter learned directly from processed online data. To facilitate the online system identification, a heuristic approach is initially proposed to preliminarily remove “musical tones” caused by conventional frame-based methods in LSF domain. Through performance evaluation based on a study of spectrogram and objective measures, it is demonstrated that the proposed post-processing scheme successfully restores natural and smooth evolution of speech dynamics, and in the meantime, effectively removes processing artifacts caused by conventional methods in various conditions.