Estimation of the short-term predictor parameters of speech under noisy conditions

Speech coding algorithms that have been developed for clean speech are often used in a noisy environment. We describe maximum a posteriori (MAP) and minimum mean square error (MMSE) techniques to estimate the clean-speech short-term predictor (STP) parameters from noisy speech. The MAP and MMSE estimates are obtained using a likelihood function computed by means of the DFT or Kalman filtering and empirical probability distributions based on multidimensional histograms. The method is assessed in terms of the resulting root mean spectral distortion between the "clean" speech STP parameters and the STP parameters computed with the proposed method from noisy speech. The estimated parameters are also applied to obtain clean speech estimates by means of a Kalman filter. The quality of the estimated speech as compared to the "clean" speech is assessed by means of subjective tests, signal-to-noise ratio improvement, and the perceptual speech quality measurement method