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Applications of OBE Algorithms to Speech Processing

  • John R. DellerJr.

Abstract

Many algorithms for identification of speech models are directly or indirectly based on linear predictive coding (LPC) analysis. LPC analysis is tantamount to identification of an autoregressive (AR) model using short-term batch processing of the observations.(1) The LPC model, therefore, is a special case of the discrete-time linear-in-parameters models treated in foregoing chapters. Accordingly, many speech processing tasks represent natural domains for applying bounded-error methods. This chapter discusses the fundamental principles requisite to application of optimal-bounded-ellipsoid (OBE) processing to problems in speech analysis, recognition and coding. The focus is the general problem of LPC identification of speech using OBE methods, including the significant issue of tracking the time-varying parameters of this very dynamic signal. Potential applications of this work in specific speech-processing endeavors include:
  1. 1.

    General modeling and analysis by predictive methods for spectral (formant) estimation, pitch detection, glottal waveform deconvolution, and pathology detection.(1)

     
  2. 2.

    Automated recognition of speech in which LPC parameters, or related parameters to which LPC coefficients are converted, are used as features in classifying phones, words, or complete messages in isolated utterances or continuous speech.

     
  3. 3.

    Speaker recognition, or speaker verification, in which the speaker’s identity is determined or verified, respectively, through parametric feature analysis.

     
  4. 4.

    Compression and synthesis of speech in which LPC parameters are used in strategies which remove redundancy in the acoustic waveform as a means of bandwidth compression or improving storage requirements. Similarly, spectral compression based on LPC analysis can be used for translation of the spectrum for hearing aids.

     

Keywords

Optimal Weight Speaker Recognition Speaker Verification Less Square Error Linear Predictive Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • John R. DellerJr.
    • 1
  1. 1.Department of Electrical EngineeringMichigan State UniversityEast LansingUSA

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