A NOVEL DEEP LEARNING TECHNIQUE FOR ALCOHOL IMPAIRMENT USING VISUAL AND ACOUSTIC FEATURES

It has been frequently demonstrated that alcohol dependence is linked to emotional deficits, notably in the interpretation of emotional facial expressions. This paper presents the findings of several researches that investigated the impact of alcohol on speech acoustic-phonetic characteristics and on video. The method for detecting intoxication in a specific suspect using facial landmarks is the subject of the proposed study. The main objective of this research paper is to acquire an understanding of detection of alcohol of individuals before they start their job. The samples from various facial landmarks using facial video sequences and speech samples audio recordings using were then subjected to perceptual and acoustic analyses; were made of individual producing lists of sentences. This paper proposes real-time comprehensive employee alcohol impairment through our algorithm. This paper presents our views on the importance of detecting alcohol impairment considering the safety and health in the workplace at a preliminary stage with state-of-art technology before even starting a job.

It is found that facial lines changed significantly after consuming alcohol and that facial landmark vectors were the most predictive features. It is believed that consumption of alcohol produces changes in the speech production that are often described as slurred speech.

Tests revealed that under the influence of alcohol has been found to be slower, lower in all amplitudes, more prone to errors at the word, sentences and phonological levels. Our experiments are based on observations at different sites and novel deep learning architecture is proposed for giving real world performance.