Recursive speech enhancement using the EM algorithm with initial conditions trained by HMM's

This paper considers speech enhancement where the speech signal is modeled with hidden filter models (HFMs), when only noisy speech signal are available. The HFM is a parametric approach for representing the speech waveform in the time domain. We apply the nested EM algorithm for jointly estimating the clean signal and the parameters of HFM and noise model. A computationally efficient implementation of the algorithm is developed by the log-likelihood gradient based on the Kalman filter output in the estimation process. The resulting algorithm does not need framing and may be viewed in the time domain context.