Real-Time Voice Interaction Engine: Architecture, Processing and Pipeline

Abstract The rapid evolution of conversational AI has created a demand for low-latency, adaptive voice interaction systems (Young et al., 2013; Brown et al., 2020). This paper proposes a modular Real-Time Voice Interaction Engine and presents an end- to-end pipeline from audio capture to syn- the sized speech output. The architecture begins with streaming Input & Capture through WebRTC/WebSocket and session control, followed by Audio Processing that performs noise cancellation, normalization, VAD, and latency checks. Streaming ASR converts speech into text with optional punctuation restoration. Natural Language Understanding applies intent detection, entity extraction, sentiment analysis, semantic parsing, and embedding generation. Retrieval modules leverage vector databases, RAG, long-term memory, database queries, and external APIs for real-time information access. Dialogue Management maintains state, applies policy rules, performs reasoning, and enables personalization. Response Generation employs large language models or hybrid templates, producing multilingual, stylistically adaptive replies. A Text-to-Speech engine synthesizes natural audio, which is then streamed back to the user. Cross- functional components such as logging, analytics, health checks, and latency monitoring ensure reliability. This work provides a blueprint for building scalable, context-aware, and production-ready voice systems capable of real-time reasoning and seamless user interaction.