We present a probabilistic description of the Harmonic plus Noise
Model (HNM) for speech signals. This probabilistic formulation permits
Maximum Likelihood (ML) parameter estimation and speech synthesis
becomes a straightforward sampling from a distribution. It also
permits development of a Kalman filter that tracks model parameters
such as pitch, harmonic amplitudes, and auto-regressive
coefficients. We focus here on pitch tracking for which the estimator
is highly non-linear. As a result it is necessary to develop an
approximate Kalman filter that goes beyond extended Kalman filtering.
