EM algorithm and Kalman filter
EM-algorithm jointly estimates the state space model parameters as well as state estimates. The E-step is a Kalman filter, which uses the current estimates to predict the new states. The M-step uses this result in an MLE procedure to obtain the parameter estimates. 

I would suggest that you read the following paper, which explains a generative model, from which all of the tools such as HMM, Kalman Filter, VQ and etc. are derived:


  Read Roweis, Ghahramani, "A Unifying Review of Linear
  Gaussian Models", Neural Computation, Vol. 11, No. 2, 1999.