Please use this identifier to cite or link to this item: https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/2858
Title: Recognizing the level of alcohol intoxication in Sri Lankan males through changes in voice suprasegmentals and reaction time
Authors: Abhayarathne, S.J.
Wakista, G.W.
Mendis, D.G.T.
Issue Date: 13-May-2015
Abstract: Alcohol affects driving by disrupting the communication of the central nervous system and there by inhibiting driver’s physical and cognitive capabilities. The current methods used to identify drunk drivers in Sri Lanka are plagued with deficiencies. Blood alcohol testing is not routinely available due to its cost and the breathalyzer balloon does not have any reproducibility or reviewability of test samples. The focus of this research is to minimize the inadequacies present in the current process and create a scientifically valid, quantifiable, objective and economical alcohol detection framework which can assist the Sri Lankan Police, Judicial Medical Officers, courts and general public. Since motor co-ordination is impaired under intoxication, it affects an individual’s speech production and reaction time. One of the common questions which arise is; whether is it possible to detect whether a person is intoxicated by observing their speech patterns and reaction time if so can it be used to determine the degree of intoxication? To test this hypothesis, healthy male native Sinhala speakers were carefully sorted out. Speech recordings, reaction time (based on a mobile game) and associated Breath Alcohol Concentration levels were taken under sober and intoxicated condition. The suprasegmental features of the audio recordings were analyzed using Praat. Several statistically significant changes were found for increasing intoxication; primary results included increased Speech Duration, Pitch, Intensity, Shimmer and Degree of Voice Breaks, while Fundamental Frequency and Harmonics to Noise Ratio were decreased. Formant, Voiced to unvoiced ratio and Jitter appeared to fluctuate towards both end under various levels of alcohol intoxication. Reaction time generally increased under alcohol intoxication but in some instances it decreased. Then after, extracted voice features were processed and imported into the proposed training model. In the evaluation phase, 66.7% of accuracy level was obtained through the trained model. A Regression Analysis was conducted to predict BrAC value using Reaction Time. However, due to the fact that Reaction Time data deviates from normal distribution, we were unable to build a statistically valid mathematical model to predict the alcohol intoxication levels. Additionally, through our research, we found that by using a mobile game based approach, an additional variable ‘practice’ gets included and in return it affects the reaction time. There has only been handful of scientific research conducted on the behavior of suprasegmental features and reaction time under intoxication. Hence, our research findings will be a valuable contribution to the scientific community. Our project is the first scientific research that is conducted on Sinhala language to identify the behavior of suprasegmental features under intoxication. Additionally, by using the data collected through research, we have built an Alcohol Language Corpus for Sinhala Language so that future researchers can further research on this area.
URI: http://hdl.handle.net/123456789/2858
Appears in Collections:BICT Group project (2014)

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