Voice AI Latency Explained: Where the Time Goes (2026)

Understanding Latency in Voice AI

In conversation, silence is highly noticeable. For voice AI agents, voice-to-voice latency—the time between a user finishing their sentence and the agent beginning to reply—is a critical metric. While humans typically have a turn-taking gap of about 200 milliseconds, voice AI builders generally aim for a latency budget of under 800 milliseconds to keep conversations feeling natural.

The Latency Budget

A typical latency budget of around 800 milliseconds is split across several steps:

• Network transit: Sending audio to and from the server.
• Endpointing: Deciding that the user has finished speaking.
• Speech-to-text: Finalizing the user's words.
• Language model: Generating the first token of the response.
• Text-to-speech: Producing the first audio.
• Playback: Playing the audio on the user's device.

The two largest contributors to latency are the language model's processing time and the endpointing decision.

Key Strategies for Reducing Latency

• Turn Detection Models: Instead of relying on a simple silence timer (which often waits 500 milliseconds or more to avoid interrupting), advanced turn detection models analyze both words and tone to determine if a user is actually finished speaking.
• Time to First Token/Audio: Rather than waiting for a full response to be generated, systems prioritize the time to first token from the language model and the time to first audio from the text-to-speech engine. The rest of the response is streamed as it is generated.
• Streaming and Overlapping: By streaming data between components, the system acts like an assembly line rather than a sequential queue. Speech-to-text sends words as they are spoken, the language model begins processing stable words early, and text-to-speech starts on the first sentence while the rest is still being written.
• Measuring Slow Turns (P95): Relying on average latency (P50) can be misleading because it hides the slowest turns. Measuring the 95th percentile (P95) helps identify the occasional slow responses that make an agent feel unreliable.

How Teams Win Time Back

To optimize performance, developers can implement several techniques:

• Locate services close to each other and close to the callers to minimize network transit time.
• Keep prompts and conversation history lean, and select models optimized for fast initial word generation.
• Use verbal fillers (e.g., "Let me check that") to fill the silence when a slow tool call is required.
• Explore speech-to-speech models that skip traditional handoffs entirely.