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Consistency among speech parameter vectors: application to predicting speech intelligibility

M H Power1, L D Braida

  • 1Massachusetts Institute of Technology, Cambridge 02139-4307, USA.

The Journal of the Acoustical Society of America
|December 1, 1996
PubMed
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A novel approach predicts speech intelligibility by analyzing consistent modifications to speech parameters using symbolic representation. This method, based on auditory models, offers a new way to assess speech degradation effects.

Area of Science:

  • Speech processing
  • Auditory modeling
  • Acoustic phonetics

Background:

  • Traditional speech intelligibility assessment relies on spectral shape preservation.
  • A new paradigm focuses on consistent transformations of speech parameters.

Purpose of the Study:

  • To introduce and evaluate a novel consistency-measurement system for predicting speech intelligibility.
  • To model intelligibility preservation based on symbolic representation of speech parameters.

Main Methods:

  • Speech parameters are quantized into symbols derived from an auditory model.
  • Intelligibility is modeled by the one-to-one correspondence between input and output symbols after transformation.
  • A consistency-measurement system was applied to linearly filtered speech and speech with additive noise.

Related Experiment Videos

  • Two parameter sets were used: band-energy values and the ensemble interval histogram (EIH) model.

    • Main Results:

    • Predictions varied monotonically with the amount of degradation within transformation classes.
    • Additive noise transformations were predicted to have a more severe effect than typically perceived.
    • The EIH parameter set showed slightly better performance in aligning predictions with human speech perception.

    Conclusions:

    • The proposed consistency-measurement system offers a new framework for predicting speech intelligibility.
    • Symbolic representation and auditory modeling provide a robust method for analyzing speech degradation.
    • The EIH model shows promise for more accurate intelligibility predictions aligned with human perception.