Articulation Entropy: An Unsupervised Measure of Articulatory Precision

Abstract

Articulatory precision is a critical factor that influences speaker intelligibility. In this paper, we propose a new measure we call ‘articulation entropy’ that serves as a proxy for the number of distinct phonemes a person produces when he or she speaks. The method is based on the observation that the ability of a speaker to achieve an articulatory target, and hence clearly produce distinct phonemes, is related to the variation of the distribution of speech features that capture articulation - the larger the variation, the larger the number of distinct phonemes produced. In contrast to previous work, the proposed method is completely unsupervised, does not require phonetic segmentation or formant estimation, and can be estimated directly from continuous speech. We evaluate the performance of this measure with several experiments on two data sets: a database of English speakers with various neurological disorders and a database of Mandarin speakers with Parkinson’s disease. The results reveal that our measure correlates with subjective evaluation of articulatory precision and reveals differences between healthy individuals and individuals with neurological impairment.