Title :
Incorporating dynamic track information for all-pole parameter estimation in noise
Author :
Ruofei Chen ; Cheung-Fat Chan
Author_Institution :
Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, China
Abstract :
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.
Keywords :
Kalman filters; autoregressive moving average processes; parameter estimation; speech enhancement; Kalman tracking scheme; all pole parameter estimation; autoregressive speech parameter estimation; dynamic track information; line spectrum frequency; natural speech evolution; speech dynamics; temporal correlation; Kalman filters; Noise measurement; Signal to noise ratio; Spectrogram; Speech; Speech processing; Kalman filter; autoregressive(AR) model; line spectrum frequencies(LSF); musical tones; temporal correlation;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
DOI :
10.1109/ICASSP.2013.6639121