Abstract
This study investigates rhythmic features based on the short-time energy function of speech signals with the aim of finding robust, speaker-independent features that indicate speaker intoxication. Data from the German Alcohol Language Corpus, which comprises read, spontaneous, and command&control speech uttered by 162 speakers of both genders and various age groups when sober and intoxicated, were analyzed. Energy contours are compared directly (Root Mean Squared Error, statistical correlation, or the Euclidean distance in the spectral space of the contour) and by parameterization of the contour using the Discrete Cosine Transform (DCT) and the first and second moments of the lower DCT spectrum. Contours are also analyzed by Principal Components Analysis aiming at fundamental "eigen contour" changes that might encode intoxication. Energy contours differ significantly with intoxication in terms of distance measures, the second and fourth DCT coefficients, and the first and second moments of the lower DCT spectrum. Principal Components Analysis did not yield interpretable "eigen contours" that could be used in distinguishing intoxicated from sober contours.
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