Joint Estimation of Formant Trajectories via Spectro-Temporal Smoothing and Bayesian Techniques

We propose a method for the joint estimation of formant trajectories from spectrograms. Formants are enhanced in the spectrograms obtained from the application of a Gammatone filterbank via a smoothing along the frequency axis. In contrast to previously published approaches, the used tracking algorithm relies on the joint distribution of formants rather than using independent tracker instances. More precisely, Bayesian mixture filtering in conjunction with adaptive frequency range segmentation as well as Bayesian smoothing are used. The algorithm was evaluated on a publicly available database containing hand-labeled formant tracks. Experimental results show a significant performance improvement compared to a state of the art approach.