Estimation of the excitation variances of speech and noise AR-models for enhanced speech coding

In this paper, we consider the estimation of short-term predictor (STP) parameters under noisy conditions. The possible autoregressive spectral shapes of the speech and additive noise are stored in AR-coefficient codebooks. The product codebook is then searched to maximize the likelihood function of the observed noisy speech signal frame. The maximum likelihood (ML) estimates of the variances of the driving term are computed for each pair of the speech and noise AR spectra. For further processing (e.g., Kalman filtering or speech coding using enhanced STP parameters), the spectra and variances that yield the maximum of the likelihood function are selected. To evaluate the proposed method, the estimates of the spectral shapes and variances are compared with those computed from clean speech signal using a common spectral distortion measure. Globally maximizing the likelihood function over some restricted region of the parameter space, the presented approach provides robust estimates