Copyright © 2007 The Institute of Electronics, Information and Communication Engineers
Regular Section -- Letters -- Speech and Hearing |
Identification of ARMA Speech Models Using an Effective Representation of Voice Source
1 The author is with the Department of Computer Science and Engineering, Shah Jalal University of Science and Technology, Sylhet 3114, Bangladesh., 2 The author is with the Department of Information and Computer Sciences, Saitama University, Saitama-shi, 338–8570 Japan. E-mail: shima{at}sie.ics.saitama-u.ac.jp
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.
Key Words: ARMA modeling, linear prediction, least square identification, glottal waveform, effective voice source
Manuscript received July 7, 2006. Manuscript revised September 29, 2006.
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