Proactive Security: Embedded AI Solution for Violent and Abusive Speech Recognition
This addresses safety concerns in violent-prone regions like Brazil by providing a discreet mobile alert system, though it appears incremental as it applies existing NLP methods to a specific domain.
The researchers developed an embedded AI system for detecting violent and abusive speech to silently alert help in dangerous situations, achieving promising results with a model under 10 MB using word embeddings and data augmentation.
Violence is an epidemic in Brazil and a problem on the rise world-wide. Mobile devices provide communication technologies which can be used to monitor and alert about violent situations. However, current solutions, like panic buttons or safe words, might increase the loss of life in violent situations. We propose an embedded artificial intelligence solution, using natural language and speech processing technology, to silently alert someone who can help in this situation. The corpus used contains 400 positive phrases and 800 negative phrases, totaling 1,200 sentences which are classified using two well-known extraction methods for natural language processing tasks: bag-of-words and word embeddings and classified with a support vector machine. We describe the proof-of-concept product in development with promising results, indicating a path towards a commercial product. More importantly we show that model improvements via word embeddings and data augmentation techniques provide an intrinsically robust model. The final embedded solution also has a small footprint of less than 10 MB.