Bayesian formant tracking using conditionally linear Gaussian models

Abstract

Formants play a central role in the perception and analysis of speech. In this presentation we describe Bayesian approaches to estimating vocal tract resonances from speech waveforms, formulated as a statistical model‐based tracking problem. In particular, approaches by Deng and colleagues have shown the promise of an extended Kalman filtering approach based on a robust linearization of the formant‐to‐cepstrum map. We describe recent extensions to model inter‐ as well as intra‐formant correlation, and detail efficient inference schemes that preserve conditional Gaussianity. A database of formant trajectories provides a notion of ground truth by which estimator performance can be evaluated, and which demonstrates the efficacy of our approach relative to contemporary benchmark tools for formant analysis.