Introducing GPT-Live

How we built a realtime system for responsive voice AI in six months

GPT-Live enables continuous voice interaction with AI, using a turnless speech model and low-latency architecture for faster, more natural conversations.

Build more natural voice experiences with GPT‑Live‑1 in the API

GPT‑Live‑1 brings natural, full-duplex voice conversations to the API, with stronger instruction following, custom voices, and telephony support.

How Tolan builds voice-first AI with GPT-5.1

Tolan built a voice-first AI companion with GPT-5. 1, combining low-latency responses, real-time context reconstruction, and memory-driven personalities for natural conversations.

Introducing gpt-realtime and Realtime API updates

We’re releasing a more advanced speech-to-speech model and new API capabilities including MCP server support, image input, and SIP phone calling support.

Customizable, no-code voice agent automation with GPT-4o

Retell AI is transforming the call center with AI voice automation powered by GPT-4o and GPT-4. 1.

Introducing Real World VoiceEQ: Measuring the human quality of voice AI

Advancing voice intelligence with new models in the API

Explore new realtime voice models in the OpenAI API that can reason, translate, and transcribe speech, enabling more natural and intelligent voice experiences.

Introducing ChatGPT Plus

We’re launching a pilot subscription plan for ChatGPT, a conversational AI that can chat with you, answer follow-up questions, and challenge incorrect assumptions.

Speaking of Voxtral

Voxtral TTS: A frontier, open-weights text-to-speech model that’s fast, instantly adaptable, and produces lifelike speech for voice agents.

Navigating the challenges and opportunities of synthetic voices

We’re sharing lessons from a small scale preview of Voice Engine, a model for creating custom voices.

GPT-5.1: A smarter, more conversational ChatGPT

We’re upgrading the GPT-5 series with warmer, more capable models and new ways to customize ChatGPT’s tone and style. GPT-5.

Training Intelligent Voice Assistant Wakeup with Controllable Synthetic Conversations

The paper presents a new wake‑up system for voice assistants that goes beyond simple keyword spotting by adding contextual trigger detection. After the wake word is heard, the system reasons to differentiate between actual user commands and unrelated speech, enabling more efficient and context‑aware interactions. A data‑generation architecture is introduced that creates a 62.3‑hour corpus of controllable multi‑speaker conversations, including direct invocations, contextual follow‑ups, and non‑addressed speech, and experimental results confirm the approach’s effectiveness across varied synthetic scenarios.