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Log-linear modeling of consonant confusion data.

T S Bell, D D Dirks, H Levitt

    The Journal of the Acoustical Society of America
    |February 1, 1986
    PubMed
    Summary
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    Log-linear models reveal how speech perception is affected by signal clarity and acoustic filtering. These models analyze consonant confusion patterns, offering insights into auditory processing and feature perception.

    Area of Science:

    • Auditory Perception
    • Speech Processing
    • Statistical Modeling

    Background:

    • Consonant confusion data is crucial for understanding speech perception.
    • Traditional analysis methods have limitations in exploring complex interactions.

    Purpose of the Study:

    • To apply log-linear models and G2 statistic to consonant confusion data.
    • To investigate interactions between error patterns and various acoustic/perceptual variables.
    • To demonstrate the utility of log-linear modeling in confusion matrix analysis.

    Main Methods:

    • Log-linear modeling and G2 statistic application.
    • Analysis of existing consonant confusion datasets.
    • Examination of interactions with signal-to-noise ratio, presentation level, vowel context, and filtering.

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    Main Results:

    • Significant interactions found between consonant error patterns and signal-to-noise ratio (S/N), presentation level, vowel context, and filtering.
    • S/N significantly altered error patterns for place of articulation, voicing, frication, and nasality.
    • Filtering affected error patterns based on specific consonant features.

    Conclusions:

    • Log-linear models effectively analyze consonant confusion matrices.
    • These models can identify specific effects, isolate variant cells, and handle collapsed matrices.
    • Log-linear techniques offer a path for developing parsimonious and predictive speech perception models.