Information Theoretic Analysis of Production-perception Efficiency: Case Study of Speech Pathology

Phonological classes define articulatory-free and articulatory-bound phone attributes. Deep neural network is used to estimate the probability of phonological classes from the speech signal. In theory, a unique combination of phone attributes form a phoneme identity. Probabilistic inference of phonological classes thus enables estimation of their compositional phoneme probabilities. A novel information theoretic framework is devised to quantify the information conveyed by each phone attribute, and assess the speech production quality for perception of phonemes. As a use case, we hypothesize that disruption in speech production leads to information loss in phone attributes, and thus confusion in phoneme identification. We quantify the amount of information loss due to dysarthric articulation available in the TORGO database. A novel information measure is formulated to evaluate the deviation from an ideal phone attribute production leading us to distinguish healthy production from pathological speech.