Fuzzy information transmission analysis for continuous speech features

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.