1. A method of tracking vocal tract resonant frequencies in a speech signal, the method comprising: a processor determining an observation probability of an observation acoustic feature vector given a set of vocal tract resonant frequencies and vocal tract resonant bandwidths, wherein the observation probability, p(o t |x t [i]) is determined as: 
 p ( o t |x t[ i ])= N ( o t ;C ( x t[ i ])+ h,D ) where o t is the observation acoustic feature vector at time t, x t [i] is the given set of vocal tract resonant frequencies and vocal tract resonant bandwidths, N(o t ;C(x t [i])+h,D) is a Gaussian distribution with a mean C(x t [i])+h and a precision D, h is a mean vector of a residual model that models differences between observation acoustic feature vectors and simulated feature vectors, D is a precision matrix of the residual model, and C(x t [i]) is a simulated feature vector determined as: C n ⁡ ( x t ⁡ [ i ] ) = ∑ k = 1 K ⁢ ⁢ 2 n ⁢ ⅇ - π ⁢ ⁢ n ⁢ b k ⁡ [ i ] f s ⁢ cos ⁡ ( 2 ⁢ ⁢ π ⁢ ⁢ n ⁢ f k ⁡ [ i ] f s ) where C n (x t [i]) is the nth element in an n order LPC-Cepstrum feature vector, K is the number of vocal tract resonant frequencies, f k is the kth vocal tract resonant frequency, b k is the kth vocal tract resonant bandwidth, and f s is a sampling frequency; a processor determining a transition probability of a transition from a first set of vocal tract resonant frequencies and vocal tract resonant bandwidths to the given set of vocal tract resonant frequencies and vocal tract resonant bandwidths based in part on a target-guided constraint for the vocal tract resonant frequencies, wherein the transition probability is calculated as: 
 p ( x t[ i ]|x t−1[ j ])= N ( x t[ i ];rx t−1 ( j )+(1 −r ) T s ,B ) where x t [i] is the given set of vocal tract resonant frequencies and vocal tract resonant bandwidths at time t, x t−1 [j] is the first set of vocal tract resonant frequencies and vocal tract resonant bandwidths at a previous time t−1, N(x t [i];rx t−1 (j)+(1−r)T s ,B) is a Gaussian distribution with mean rx t−1 (j)+(1−r)T s and precision B, r is a rate, and T s is a target that is tied to a speech unit s associated with time t for the vocal tract resonant frequencies and vocal tract resonant bandwidths; and a processor using the observation probability and the transition probability to select a set of vocal tract resonant frequencies corresponding to the observation acoustic feature vector.