Formant tracking by mixture state particle filter

This paper presents a mixture state particle filter method for formant tracking during both vowels and consonants. We show that mixture state particle filter model is able to incorporate prior information about phoneme class into the system, which helps the system to find global optimal solutions. Formant frequencies are defined as eigenfrequencies of the vocal tract in this paper, and by exploring this fact using spectral estimation techniques, the observation PDF of the particle filter can be simplified. We show that by using this likelihood function in the importance weights, the system is able to track the formants using a small number of particles.