Neural networks’ posterior probability as measure of effects of alcohol on speech

Abstract

Intoxication has a well-known effect on speech production. Lester and Skousen (1974) reported that the place of articulation for /s/ is retracted and /tʃ/ and /ʤ/ are deaffricated (i.e., substituted by a non-affricate segment) in drunken speech. Zihlmann (2017) further established the robustness of deaffrication as it cannot be consciously suppressed under intoxication. Using these prevalent speech errors as test cases, this study extends a phonologically-informed neural network approach to the study of intoxicated speech. The approach has success in measuring pathological speech and lenition patterns in healthy speakers. Degrees of place retraction for /s/ and deaffrication of /tʃ/ and /ʤ/ are estimated from posterior probabilities calculated by recurrent neural networks trained to recognize [anterior], [continuant] and [strident] features. When applied to a corpus of alcohol English speech, preliminary results suggested that sober versus drunken state could be reliably predicted by the three posterior probabilities. The directions of the effects are largely in line with previous studies. For example, /tʃ/ and /ʤ/ are more fricated (higher strident and continuant probabilities), and /s/ is more retracted (lower anterior probability) in drunken compared to sober speech. The results suggest that the intoxicated speech can be reliably quantified by this new approach.