Performance of BAM algorithm and Viterbi algorithm for lie detection system using voice

Abstract

Lie detection systems using voice can be detected using the Bidirectional Associative Memory (BAM) algorithm. However, the accuracy of the system by using BAM is still low, where the true detection results obtained show a small percentage and this is also influenced by the number of training voice samples. In this study, the authors propose solutions using the Viterbi algorithm in detecting lies through sound. This research carried out the process of introducing and training lie voice samples on the word go, walk and move and then tested by simulating it in the exercise data and test data to generate the percentage of recognition and classification of the voice of the lie. The results showed that the lam detection system using the BAM algorithm has a true detection range of 67% for the word go, 72% on the word walk and 69% on the word moved. While using the Viterbi algorithm has a true detection range of 92% on the word go, 90% on the word path and 88% on the word moved. The results of this study show that Viterbi algorithm is quite effective applied to voice recognition system, can be seen from the relatively good level of accuracy and the use of low memory resources.