Faculty Advisor

Agu, Emmanuel O.

Abstract

Driving under the influence is one of the largest risk factors leading to accidents. Intoxication manifests in the drinker's voice. This paper explores deep learning architectures and hand extracted features to classify voice samples as either intoxicated of sober. Our method classifies intoxicated speech with an unweighted average recall of 59.2%.

Publisher

Worcester Polytechnic Institute

Date Accepted

2020-04-07

Major

Computer Science

Project Type

Major Qualifying Project

Accessibility

Unrestricted

Advisor Department

Computer Science

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