Introduction to AI voice agents
Hey, Bala again. In this session, Manikantha S from Sarvam AI takes us through the evolution of voice agents, current status and the future.
Key Takeaways
- Voice agents have significantly improved due to advancements in speech recognition and language models (LLMs)
- Building effective voice agents requires careful scoping, prompt engineering, and consideration of latency
- The ecosystem around voice agents (analytics, testing, evals) is still evolving and presents opportunities
- Voice agents are seeing successful production deployments, with comparable or better conversion rates than humans in some cases
Voice Agent Architecture
- Components: Automatic Speech Recognition (ASR), Translation, Language Model (LLM), Text-to-Speech (TTS)
- Typical latency breakdown: 50% LLM, 40% speech models, 10% translation
- Challenges include handling background noise, interruptions, and voice activity detection
- Current focus on reducing latency through model optimization, caching, and streaming techniques
Building Voice Agents
- OpenAI's platform (platform.openai.com) offers a simple way to create assistants
- Playground feature allows testing of voice agents with different voices and models
- WebRTC integration possible for deploying agents on websites or telephony providers
Voice Agent Ecosystem
- Analytics: Still evolving, need for better tools to understand conversation metrics and business insights
- Testing: Manual testing common, but automated testing becoming more important for scalability
- Evals: Underlooked area, crucial for assessing model performance and conversation quality
Current Adoption and Performance
- Successfully deployed for large-scale use cases (e.g., Aadhaar customer service)
- Companies have handled tens of millions of calls using voice agents
- Conversion rates comparable or better than human agents (2.7-4.5% vs 3-4% for manual)
- Cost savings of around 50% reported
Future Developments
- Voice-to-voice systems eliminating need for separate ASR, TTS, and LLM components
- Cross-platform integration for seamless user experience across different channels
- Increased memory and context length for more personalized and complex interactions
- Broader context understanding, moving beyond narrow use cases
- Automatic syncing with developer documentation for easier maintenance
Emerging Use Cases
- Companion/friend voice agents
- Educational applications (teachers, mentors, guides)
- Faith tech and spirituality-related voice agents
- Astrology and other niche applications
Next Steps
- Attendees can explore building voice agents using OpenAI's platform
- Connect with Manikantha on LinkedIn for further discussions
- Watch for public release of Sarvam AI's platform for voice agent development
- Review additional reading materials on VC insights into voice agent market trends
Here's the entire recording of the session.