Alignment with DF goals (BGI, Platform growth, community)

In many conflict-prone regions, especially in areas with low literacy, voice communication through radio and messaging apps like WhatsApp is the primary way tension and hate speech spread. Text-based monitoring often misses early verbal cues of unrest, especially when they're expressed in local dialects or emotionally charged tones. There’s a need for a voice-first AI tool that listens, understands, and diffuses conflict in culturally appropriate ways before it escalates.

Problem description

Current AI conflict monitoring systems focus heavily on text and major languages, overlooking audio-based communication in local dialects where many conflicts begin. Without early detection of spoken aggression, peacebuilders miss critical intervention windows.

Proposed Solutions

The AI will:

Use audio emotion detection and speech recognition to monitor public radio, community calls, and voice messages in native dialects

Identify spikes in anger, fear, or hate speech using tone, context, and language.

Generate calming, culturally relevant counter-audio responses using trusted voice styles (e.g., elders, community leaders).

Notify local peace mediators through a dashboard when intervention is needed.

Ensure data privacy and opt-in participation for monitored sources.