IEICE Trans - Identification of ARMA Speech Models Using an Effective Representation of Voice Source

Identification of ARMA Speech Models Using an Effective Representation of Voice Source

M. Shahidur RAHMAN
Tetsuya SHIMAMURA

Publication
IEICE TRANSACTIONS on Information and Systems Vol.E90-D No.5 pp.863-867
Publication Date: 2007/05/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e90-d.5.863
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Speech and Hearing
Keyword:
ARMA modeling, linear prediction, least square identification, glottal waveform, effective voice source,

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Summary:
A two-stage least square identification method is proposed for estimating ARMA (autoregressive moving average) coefficients from speech signals. A pulse-train like input sequence is often employed to account for the source effects in estimating vocal tract parameters of voiced speech. Due to glottal and radiation effects, the pulse train, however, does not represent the effective voice source. The authors have already proposed a simple but effective model of voice source for estimating AR (autoregressive) coefficients. This letter extends our approach to ARMA analysis to wider varieties of speech sounds including nasal vowels and consonants. Analysis results on both synthetic and natural nasal speech are presented to demonstrate the analysis ability of the method.

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