Recursive tracking of formants in speech signals

We report on an approach to recursively track parameters of a cascade formant model. The work follows from that of Rigoll (1986) who showed how an extended Kalman filter (EKF) may be used for recursive estimation of formants. The success of this approach depends on our ability to tune the model noise variances properly. The approach also fails when there is a mismatch between the complexity of the data and that of the model (i.e. wrong number of formants). We show how a multiple model (MM) approach may be used to overcome these problems. We run several models in parallel and use the innovation probabilities of the EKF to recursively evaluate the likelihoods of each of the models. Experimental results demonstrate the feasibility of the approach; accurate switching between models and good tracking of the formants is achieved