Log-linear modeling of consonant confusion data.
Log-linear models, in conjunction with the G2 statistic, were developed and applied to several existing sets of consonant confusion data. Significant interactions of consonant error patterns were found with signal-to-noise ratio (S/N), presentation level, vowel context, and low-pass and high-pass filtering. These variables also showed significant interactions with error patterns when categorized on the basis of feature classifications. Patterns of errors were significantly altered by S/N for place of articulation (front, middle, back), voicing, frication, and nasality. Low-pass filtering significantly affected error patterns when categorized by place of articulation, duration, or nasality; whereas, high-pass filtering only affected voicing and frication error patterns. This paper also demonstrates the utility of log-linear modeling techniques in applications to confusion matrix analysis: specific effects can be tested; variant cells in a matrix can be isolated with respect to a particular model of interest; diagonal cells can be eliminated from the analysis; and the matrix can be collapsed across levels of variables, with no violation of independence. Finally, log-linear techniques are suggested for development of parsimonious and predictive models of speech perception.
D006801:Humans; D010700:Phonetics; D011571:Psychoacoustics; D013067:Speech Perception