Anoop Dawar from Deepgram discusses the evolution of voice AI, from basic transcription to sophisticated, real-time voice agents. He covers the key technical challenges in production, such as latency and interruption handling, and introduces Deepgram's Flux system. The talk concludes with a look at the future of speech-to-speech models that can understand emotional nuance, moving closer to passing the audio Turing Test.
A deep dive into building sophisticated voice agents using OpenAI's Realtime API and Agents SDK. The session covers architectural patterns like chained vs. end-to-end models, the use of multi-agent systems with handoffs for specialized tasks, and best practices for production including debugging with traces, implementing guardrails, and creating robust evaluations.
An overview of the new GPT-realtime speech-to-speech model and the general availability of the Real-Time API, detailing its architecture, advanced capabilities like image input and multilingualism, training methodology, and new enterprise-ready features.
A deep dive into the challenges of building production-grade, low-latency voice AI agents, and how the open-source, vendor-neutral framework Pipecat provides a comprehensive solution for development, deployment, and scaling. Learn about voice AI architecture, the trade-offs between speech-to-speech and text-based models, and practical deployment strategies.
Yishay Carmiel and Roy Zanbel of Apollo Defend explore the state of voice AI, detailing the shift from cascaded models to end-to-end speech-to-speech systems. They break down the imminent security threats, including accessible voice cloning and sophisticated agent-based attacks, and discuss the nascent defense mechanisms and the urgent need for a new layer of voice security for governments, enterprises, and consumers.