Spam Detection Over Call Transcript Using Deep Learning

An increasing volume of malicious spam calls has ushered an urgent need for a system that can automatically detect them. A robust spam call detection system must identify and block spam calls with high accuracy. Unlike current techniques that use crowd sourcing and are prone to phone number spoofing, this paper proposes a novel deep learning-based approach to classify phone calls, as spam or ham, based on the generated call transcript. This paper proposes a means of classification using embedded features with a 2-Path Hybrid Model (2PHM), comprising of a Long Short Term Memory Network (LSTM) and a Sentence Similarity Network. The 2PHM uses the transcript generated from the call by converting it into feature vectors, embedded in a high dimensional latent space, using the Universal Sentence Encoder embedding technique. Both paths of the hybrid model generate results that are weighed in an 85:15 ratio to finally generate a classification. The proposed system has been tested against Machine Learning models like Naive Bayes, K-Nearest Neighbors, Random Forests and Support Vector Machines and performs better than them, with an overall classification accuracy of 93% and a latency of 3 s.