Voice Agents and Real-Time Conversation

Voice Agent Architecture

[User Speech]
     |
     v
[Speech-to-Text] (Whisper, Deepgram)
     |
     v
[LLM Processing] (GPT-4, Claude)
     |
     v
[Text-to-Speech] (ElevenLabs, OpenAI)
     |
     v
[Audio Output]

Key Metrics

Real-Time API (OpenAI)

from openai import OpenAI

client = OpenAI()

# Real-time conversation API (conceptual)
session = client.realtime.create_session(
    voice="nova",
    model="gpt-4o-realtime"
)

# Bidirectional audio streaming
async for event in session:
    if event.type == "audio_chunk":
        play_audio(event.audio)
    elif event.type == "transcript":
        print(f"User: {event.text}")

Latency Optimization

Turn-Taking

class VoiceAgent:
    def __init__(self):
        self.is_speaking = False
        self.vad = VoiceActivityDetector()

    async def handle_audio(self, audio_chunk):
        if self.is_speaking and self.vad.is_speech(audio_chunk):
            # User interruption
            await self.stop_speaking()
            await self.process_interruption(audio_chunk)

Use Cases

Platforms

Challenges

• Managing interruptions gracefully
• Handling noise and multiple speakers
• Cross-talk in phone calls
• Maintaining conversation context
• Latency across components