LPC SPEECH ANALYSIS USING THE L//1 NORM.

Richard J. Mammone, Kent Wang, Steven Gay

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

A new method is presented for low bit encoding. The approach taken is similar to that of LPC analysis in that an autoregressive model of the vocal tract is used. The estimate of the model is made using a least absolute value (LAV) criterion. That is, the sum of the absolute values (L//1 norm) of the error is minimized. The approach is compared with the usual least squares (L//2 norm) methods, i. e. , covariance and autocorrelation. The minimum L//1 estimate was obtained using the simplex method of linear programming. It is well known that the L//1 norm estimate is highly robust to statistical outliers. The robust nature of the minimum L//1 algorithm can be interpreted as an expectation that the residual will consist of a series of impulses. This expectation is of course valid for speech, as demonstrated by the improved performance provided by multipulse LPC.

Original languageEnglish (US)
Pages (from-to)485-488
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 1985

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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