DocumentCode :
1843685
Title :
All-pole modeling of speech based on the minimum variance distortionless response spectrum
Author :
Haurthi, M.N. ; Rao, Bhaskar D.
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA
Volume :
2
fYear :
1997
fDate :
2-5 Nov. 1997
Firstpage :
1061
Abstract :
We develop more fully all-pole modeling of speech based on the minimum variance distortionless response (MVDR) spectrum. It is shown that MVDR modeling provides a class of all-pole models that are flexible for tackling a wide variety of speech modeling objectives. In particular the high order MVDR spectrum provides a robust model for all types of speech including voiced speech, unvoiced speech, and mixed spectra. Furthermore, it is simply obtained, and is always superior to the linear prediction (LP) spectrum. With its high quality modeling, the high order MVDR spectrum is suitable for use as a high quality reference spectrum, or for applications like speech recognition. In addition, the MVDR model possesses flexibility for developing low order all-pole models suitable for compression applications. In particular; reduced order MVDR all-pole models are shown to often outperform conventional LP filters in modeling all types of speech spectra. For more accurate modeling of a set of speech spectral samples in the frequency domain, MVDR modeling facilitates the development of superior weighted all-pole filters.
Keywords :
filtering theory; poles and zeros; spectral analysis; speech processing; speech recognition; LP filters; compression applications; frequency domain; high order MVDR spectrum; high quality reference spectrum; linear prediction; low order all-pole models; minimum variance distortionless response; mixed spectra; reduced order MVDR all-pole models; speech modeling; speech processing; speech recognition; speech spectral samples; unvoiced speech; voiced speech; weighted all-pole filters; Array signal processing; Design methodology; Frequency domain analysis; Log periodic antennas; Nonlinear filters; Predictive models; Robustness; Spectral analysis; Speech processing; Speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signals, Systems & Computers, 1997. Conference Record of the Thirty-First Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
ISSN :
1058-6393
Print_ISBN :
0-8186-8316-3
Type :
conf
DOI :
10.1109/ACSSC.1997.679068
Filename :
679068
Link To Document :
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