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

Alcohol, a progressive central nervous system depressant, has been found to negatively affect not only cognitive functions but also the production of speech—a complex motor activity requiring a high degree of coordination. In this study, we estimate the degrees of deaffrication, spirantization, and retracted place of articulation for /t/, /d/, /s/, /ʃ /, /tʃ /, and /ʤ/ in a corpus of speech affected by alcohol. These estimations are based on posterior probabilities calculated by recurrent neural networks known as Phonet, which are trained to recognize anterior, continuant, and strident phonological features. The results obtained revealed both categorical and gradient errors in intoxicated speech, indicating the reliability of Phonet in quantifying fine-grained errors.