Contact Center Voice AI: Low-Latency Intelligence Extraction from Messy Audio Streams — Dippu Singh
Apr 8, 2026 · 22:56
Dippu Singh of Fujitsu North America presents an architecture for real-time voice intelligence in contact centers that reduces post-call work by 50% through structured intent extraction from messy audio streams. The pipeline comprises four stages: voice capture with stereo channel splitting and PII masking, speech-to-text with domain-specific dictionaries, a generative AI core that uses system prompts to output separate JSON bullet points for customer intent and operator actions, and a customer data sync layer that maps LLM output to CRM fields via REST APIs. Key results show after-call work (ACW) dropped from 6.3 to 3.1 minutes, while data entry quality became standardized. Current constraints include STT accuracy for heavy accents, API token costs for long transcripts, and security compliance overhead. The roadmap targets explainable AI for agent coaching, predictive staffing from categorized intent data, and real-time abusive-call detection to protect operators.
