Abstract:
Feature information transmission analysis (FITA) estimates information transmitted by an acoustic
feature by assigning tokens to categories according to the feature under investigation and
comparing within-category to between-category confusions. FITA was initially developed for
categorical features (e.g., voicing) for which the category assignments arise from the feature
definition. When used with continuous features (e.g., formants), it may happen that pairs of tokens
in different categories are more similar than pairs of tokens in the same category. The estimated
transmitted information may be sensitive to category boundary location and the selected number of
categories. This paper proposes a fuzzy approach to FITA that provides a smoother transition
between categories and compares its sensitivity to grouping parameters with that of the traditional
approach. The fuzzy FITA was found to be sufficiently robust to boundary location to allow
automation of category boundary selection. Traditional and fuzzy FITA were found to be sensitive
to the number of categories. This is inherent to the mechanism of isolating a feature by dividing
tokens into categories, so that transmitted information values calculated using different numbers of
categories should not be compared. Four categories are recommended for continuous features when
twelve tokens are used.
