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Evaluating relative speech recognition performance using the proficiency factor and rationalized arcsine differences

G A Studebaker1, D M McDaniel, R L Sherbecoe

  • 1School of Audiology and Speech-Language Pathology, University of Memphis, Tennessee 38105, USA.

Journal of the American Academy of Audiology
|March 1, 1995
PubMed
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Rationalized arcsine differences statistically outperform proficiency factors for comparing speech intelligibility test scores. Proficiency factors are useful for adjusting the articulation index (AI) for individual subject variables.

Area of Science:

  • Audiology
  • Speech Science
  • Psychoacoustics

Background:

  • The articulation index (AI) predicts speech intelligibility.
  • Comparing predicted and observed speech recognition scores requires robust measures.
  • Existing measures include proficiency factors and rationalized arcsine differences.

Purpose of the Study:

  • To evaluate and compare the statistical efficacy of proficiency factors and rationalized arcsine differences.
  • To determine the optimal application of each measure in speech intelligibility assessments.

Main Methods:

  • Calculated proficiency factors as the ratio of AI from test scores to AI from objective measurements.
  • Calculated rationalized arcsine differences between observed and AI-predicted scores.
  • Statistically compared the two measures.

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Main Results:

  • Rationalized arcsine differences demonstrated statistical superiority over proficiency factors for score comparison.
  • Proficiency factors were found to be effective for correcting the AI for subject-specific variables.

Conclusions:

  • Rationalized arcsine differences are the preferred method for direct comparison of speech intelligibility scores.
  • Proficiency factors serve a valuable role in refining the AI by accounting for individual differences.
  • A combined approach leveraging the strengths of both measures is recommended for comprehensive speech assessment.