Comparison of auto-regressive, non-stationary excited signal parameter estimation methods

Akira Sasou, Masataka Goto, Satoru Hayamizu, Kazuyo Tanaka

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

4 Citations (Scopus)

Abstract

Previously, we proposed an Auto-Regressive Hidden Markov Model (AR-HMM) and an accompanying parameter estimation method. An AR-HMM was obtained by combining an AR process with an HMM introduced as a non-stationary excitation model. We demonstrated that the AR-HMM can accurately estimate the characteristics of both articulatory systems and excitation signals from high-pitched speech. As the parameter estimation method iteratively executes learning processes of HMM parameters, the proposed method was calculation-intensive. Here, we propose two novel kinds of auto-regressive, non-stationary excited signal parameter estimation methods to reduce the amount of calculation required.

Original languageEnglish
Title of host publicationMachine Learning for Signal Processing XIV - Proceedings of 2004 IEEE Signal Processing Society Workshop
EditorsA. Barros, J. Principe, J. Larsen, T. Adali, S. Douglas
Pages295-304
Number of pages10
Publication statusPublished - 2004
Externally publishedYes
EventMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop - Sao Luis, Brazil
Duration: 2004 Sep 292004 Oct 1

Publication series

NameMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop

Conference

ConferenceMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop
Country/TerritoryBrazil
CitySao Luis
Period04/9/2904/10/1

ASJC Scopus subject areas

  • Engineering(all)

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