Rhythm and formant features for automatic alcohol detection

Two speech feature sets, RMS rhythmicity and formant frequencies F1-F4, are analyzed for their ability to distinguish alcoholized from sober speech. We describe the statistical framework based on the Alcohol Language Corpus (ALC), including other factors such as gender, age and speaking style, and its application to our case. Rhythm features are calculated using a new method based solely on the short-time energy function; formant features are derived using the standard formant tracker SNACK. Our findings indicate that 3 rhythm and 3 formant features have a high potential to detect intoxication within the speech data of a subject. We also tested the hypothesis that vowels are more centralized in the F1/F2 space for alcoholized speech, but found that, on the contrary, subjects tend to hyperarticulate when being tested for intoxication.