TY - GEN

T1 - Particle filtering approach to Bayesian formant tracking

AU - Zheng, Yanli

AU - Hasegawa-Johnson, M.

N1 - Publisher Copyright:
© 2003 IEEE.

PY - 2003

Y1 - 2003

N2 - 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.

AB - 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.

KW - Acoustic measurements

KW - Bayesian methods

KW - Filtering

KW - Frequency estimation

KW - Frequency synthesizers

KW - Particle tracking

KW - Poles and zeros

KW - Signal processing algorithms

KW - Speech

KW - Stochastic processes

UR - http://www.scopus.com/inward/record.url?scp=51849093650&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=51849093650&partnerID=8YFLogxK

U2 - 10.1109/SSP.2003.1289549

DO - 10.1109/SSP.2003.1289549

M3 - Conference contribution

AN - SCOPUS:51849093650

T3 - IEEE Workshop on Statistical Signal Processing Proceedings

SP - 601

EP - 604

BT - Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003

PB - IEEE Computer Society

T2 - IEEE Workshop on Statistical Signal Processing, SSP 2003

Y2 - 28 September 2003 through 1 October 2003

ER -