Formant tracking by mixture state particle filter

Research output: Contribution to journalConference article

Abstract

This paper presents a mixture state particle filter method for formant tracking during both vowels and consonants. We show that mixture state particle filter model is able to incorporate prior information about phoneme class into the system, which helps the system to find global optimal solutions. Formant frequencies are defined as eigenfrequencies of the vocal tract in this paper, and by exploring this fact using spectral estimation techniques, the observation PDF of the particle filter can be simplified. We show that by using this likelihood function in the importance weights, the system is able to track the formants using a small number of particles.

Original languageEnglish (US)
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume1
StatePublished - Sep 28 2004
EventProceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada
Duration: May 17 2004May 21 2004

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

@article{7a3c3ea1b99b4386a4f5f1feb0ae3b49,
title = "Formant tracking by mixture state particle filter",
abstract = "This paper presents a mixture state particle filter method for formant tracking during both vowels and consonants. We show that mixture state particle filter model is able to incorporate prior information about phoneme class into the system, which helps the system to find global optimal solutions. Formant frequencies are defined as eigenfrequencies of the vocal tract in this paper, and by exploring this fact using spectral estimation techniques, the observation PDF of the particle filter can be simplified. We show that by using this likelihood function in the importance weights, the system is able to track the formants using a small number of particles.",
author = "Yanli Zheng and Hasegawa-Johnson, {Mark Allan}",
year = "2004",
month = "9",
day = "28",
language = "English (US)",
volume = "1",
journal = "Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing",
issn = "0736-7791",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Formant tracking by mixture state particle filter

AU - Zheng, Yanli

AU - Hasegawa-Johnson, Mark Allan

PY - 2004/9/28

Y1 - 2004/9/28

N2 - This paper presents a mixture state particle filter method for formant tracking during both vowels and consonants. We show that mixture state particle filter model is able to incorporate prior information about phoneme class into the system, which helps the system to find global optimal solutions. Formant frequencies are defined as eigenfrequencies of the vocal tract in this paper, and by exploring this fact using spectral estimation techniques, the observation PDF of the particle filter can be simplified. We show that by using this likelihood function in the importance weights, the system is able to track the formants using a small number of particles.

AB - This paper presents a mixture state particle filter method for formant tracking during both vowels and consonants. We show that mixture state particle filter model is able to incorporate prior information about phoneme class into the system, which helps the system to find global optimal solutions. Formant frequencies are defined as eigenfrequencies of the vocal tract in this paper, and by exploring this fact using spectral estimation techniques, the observation PDF of the particle filter can be simplified. We show that by using this likelihood function in the importance weights, the system is able to track the formants using a small number of particles.

UR - http://www.scopus.com/inward/record.url?scp=4544278205&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=4544278205&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:4544278205

VL - 1

JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

SN - 0736-7791

ER -