Auditory-based Formant Estimation in Noise using a Probabilistic Framework

We recently introduced a computationally efficient framework for tracking formants which combines a biologically inspired preprocessing for enhancing formants in spectrograms with a probabilistic framework for estimating formant trajectories. In contrast to previously published approaches our tracking scheme relies on the joint distribution of formants rather than using independent tracking instances for each formant separately. Therewith more precise formant estimates could be obtained. In this paper we will briefly review our algorithm and extend it by using more sophisticated models of the formants underlying dynamics. Furthermore, we will dwell on the robustness of our method for speech degraded by various types of noise. A comprehensive evaluation on a large publicly available database containing hand-labeled formant trajectories shows significant performance improvements in both clean and noisy speech compared to state of the art approaches.