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Evaluating the articulation index for auditory-visual input.

K W Grant1, L D Braida

  • 1Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge 02139.

The Journal of the Acoustical Society of America
|June 1, 1991
PubMed
Summary
This summary is machine-generated.

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The ANSI standard for auditory-visual (AV) articulation index (AI) generally holds but shows underprediction for low AI values and overprediction for high AI values. Synergistic effects in filtered speech deviate from additivity assumptions.

Area of Science:

  • Acoustics
  • Speech Perception
  • Signal Processing

Background:

  • The ANSI standard (ANSI S3.5-1969 R1986) provides a procedure for calculating the auditory-visual (AV) articulation index (AI).
  • Accurate AI prediction is crucial for understanding speech intelligibility in various acoustic conditions.

Purpose of the Study:

  • To investigate the validity of the ANSI standard's AV AI correction procedure.
  • To evaluate auditory (A), visual (V), and AV sentence identification under different speech conditions.

Main Methods:

  • Tested wideband speech with additive noise and bandpass-filtered speech in quiet and noise.
  • Measured sentence identification scores for auditory, visual, and combined auditory-visual conditions.
  • Compared experimental AV AI scores against predictions from the ANSI standard.

Related Experiment Videos

Main Results:

  • Averaged data across subjects and talkers supported the ANSI standard's procedure.
  • Deviations were observed for individual subjects and talkers.
  • The standard underpredicted AV scores for filtered speech with AI < 0.25 and overpredicted for AI > 0.25.
  • Synergistic effects (non-additive AI) were found for non-adjacent filter-band combinations.

Conclusions:

  • The ANSI standard's AV AI procedure is generally supported but requires refinement for specific signal types and AI ranges.
  • Observed synergistic effects challenge the additivity assumption of Articulation Theory, aligning with prior research.
  • Further research is needed to account for these deviations and improve AI prediction accuracy.