Full-Duplex Voice AI Application Tech Selection & Architecture Design Consultant

You are a Voice AI systems architect with deep expertise in real-time, full-duplex voice interaction systems. Help me design a production-ready full-duplex voice AI application. ## Requirements - Use case: [e.g., AI phone agent, voice assistant, real-time interpreter] - Target latency: [e.g., <500ms end-to-end] - Concurrent users: [expected scale] - Languages: [supported languages] - Deployment: [cloud/edge/hybrid] - Budget tier: [startup/enterprise] ## Please provide: ### 1. Technology Stack Comparison Compare these options with a decision matrix (latency, cost, quality, language support): - STT: Whisper (local) vs Deepgram vs Google STT vs Azure Speech - LLM: GPT-4o-realtime vs Claude vs Gemini Live vs local models - TTS: ElevenLabs vs PlayHT vs Azure Neural TTS vs Coqui/StyleTTS2 vs VibeVoice - Transport: WebRTC vs WebSocket vs gRPC streaming ### 2. Architecture Design - System architecture diagram (Mermaid) - Audio pipeline: capture - VAD - STT - LLM - TTS - playback - Interruption handling strategy (barge-in detection) - Echo cancellation and noise suppression approach - State machine for conversation turn management ### 3. Latency Optimization - Streaming STT with partial results - LLM streaming with TTS chunking - Audio buffer management - Speculative TTS generation - Connection pooling and warm-up strategies ### 4. Production Considerations - Graceful degradation when services are slow - Monitoring and observability (latency percentiles, error rates) - Cost estimation per minute of conversation - Compliance (call recording, GDPR, data residency) ### 5. Implementation Skeleton Provide a Python/TypeScript code skeleton for the core audio pipeline with: - WebSocket server setup - VAD integration - Streaming STT to LLM to TTS pipeline - Interruption handling Be specific about trade-offs. Recommend the best option for my requirements, not just list all options.