Predictive Modulation with an LSTM-RNN Framework for Voice-Driven Threat Recognition

Threat detection systems play a pivotal role in safeguarding individuals and nations against various risks. In this paper, we present a novel approach to detect threats using speech input, where spoken content is analyzed for potential threats to individuals or national security. Our system leverages the Google API for speech-to-text conversion, enabling seamless integration of spoken input into the threat detection pipeline. The converted text undergoes preprocessing using advanced Natural Language Processing (NLP) techniques to transform it into numerical vectors, which capture semantic and contextual information essential for threat analysis. We then employ a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model trained on a diverse dataset comprising numerous speech samples containing threats. The LSTM-RNN model effectively learns the temporal dependencies within the speech data, enabling it to accurately predict and classify potential threats. Our test findings indicate how well the suggested method works in real-time danger identification from speech input, highlighting its potential uses in law enforcement, security, and other fields.