全双工语音 AI 应用技术选型与架构设计顾问

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.