Particle filtering approach to Bayesian formant tracking

Yanli Zheng, M. Hasegawa-Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper proposes a formant tracker capable of computing the maximum a posteriori probability formant frequencies (eigenfrequencies of the vocal tract) during periods of consonant closure. Two specific novel algorithms are proposed. First, an exponentially weighted autoregressive (EWAR) spectral model is proposed. The EWAR model is capable of modeling the peak amplitudes, bandwidths, and frequencies in an ARMA spectral model without any explicit model of the spectral zeros. Instead of explicit zero models, the amplitudes of spectral peaks are adjusted by exponential coupling weights. It is demonstrated that the parameters of the EWAR model may be efficiently computed from the observed speech cepstrum. Second, the smoothness of formant frequency trajectories is modeled using a linear dynamic systems model with a nonlinear output map, and maximum a posteriori probability tracking of dynamic formant frequencies is demonstrated using a particle filtering approach.

Original languageEnglish (US)
Title of host publicationProceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)0780379977
StatePublished - 2003
EventIEEE Workshop on Statistical Signal Processing, SSP 2003 - St. Louis, United States
Duration: Sep 28 2003Oct 1 2003

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings


OtherIEEE Workshop on Statistical Signal Processing, SSP 2003
Country/TerritoryUnited States
CitySt. Louis


  • Acoustic measurements
  • Bayesian methods
  • Filtering
  • Frequency estimation
  • Frequency synthesizers
  • Particle tracking
  • Poles and zeros
  • Signal processing algorithms
  • Speech
  • Stochastic processes

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications


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