Cracking the < 1-second Voice Loop: What We Learned After 30+ Stack Benchmarks

Introduction — Why We’re Racing for Sub-Second Voice Loops

In October 2024 OpenAI unveiled its Realtime API, the first end-to-end multimodal model able to convert speech → text → reasoning → speech fast enough to feel human.

That launch set the hype machine spinning: “Why bother wiring three engines together when a single neural giant can do voice-to-voice in one shot?”

Reality check:

Meanwhile, the open-source and vendor ecosystem didn’t sit still. By mid-2025 we could stitch together Deepgram STT + GPT-4 Nano/Mini + Cartesia Sonic (or ElevenLabs) and hit similar latency for a fraction of the cost — while choosing any voice we like.

The trick is to keep every stage modular:

• Speech-to-Text (STT) — use whatever recognizer is fastest or cheapest today.
• Large Language Model (LLM) — swap Mini ↔ Nano ↔ Flash checkpoints as they evolve.
• Text-to-Speech (TTS) — pick the voice library that matches your brand.

Enter LiveKit

The glue that lets us shuffle those building blocks in real time is LiveKit — a WebRTC orchestration layer with an SDK that can fan-out telephone legs, browser streams, and AI workers on the same SFU.

New STT, LLM, or TTS drops on a Friday?

We just swap the block, restart the worker, and it's live by lunch.

No retraining. No monolithic rebuilds. Just composable parts evolving at their own pace.

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What “Latency” Really Means (and Why It Hurts)

Human turn-taking is fast. Large-scale multilingual studies show that the median inter-turn gap is ≈ 200 ms, but the range spans from as low as 7 ms (in Japanese) to over 440 ms (in Danish), depending on the language, sentence structure, and context of the exchange [1].

A replication focused on English measured an average gap of 236 ms ± 520 ms SD, confirming that even within a single language, there’s wide variance depending on interaction type and formality.

When the silence between turns stretches, our perception shifts:

These reference points form the benchmark we’re chasing:

get the bot’s first syllable inside the ~400 ms comfort zone—or, at the very least, close enough that the pause doesn’t break the conversational rhythm.

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Anatomy of a Voice Pipeline

A real-time loop has three streaming stages that run strictly in series:

Key observation: in every stack we measured, LLM TTFT + TTS TTFB account for 90 %+ of total loop time; with streaming recognizers, STT is effectively negligible.

All three stages run in streaming inference — we start passing tokens or audio frames downstream the moment we see them.

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The Latency / Quality / Cost Triangle

Push one corner, the others move:

• Lower latency ⇢ smaller / quantized models, “good-enough” neural voices.
• Higher quality ⇢ bigger LLMs, premium TTS; usually slower.
• Lower cost ⇢ open-source or micro-models; may ding both speed and fidelity.

Our job is to find the quickest loop that still sounds customer-ready and doesn’t torch the budget.

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How We Benchmarked

• Same system prompt in English and Spanish.
• Dozens of STT + LLM + TTS combinations (cloud & OSS of which we have selected the top performing).
• LiveKit measured STT duration, TTFT, TTFB on every turn.

A few things we learned fast

1. LLMs & TTS slow down outside English.
2. A long system prompt only punishes the first turn (~ +300 ms); later turns ride the KV-cache.
3. The newest “nano” LLMs plus an ultra-fast TTS can get that first syllable under 800 ms, scraping the human comfort ceiling.

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What the Numbers Tell Us

1. First-Turn Overhead Is Real

Every stack shows a heavier first turn because the LLM must ingest the entire system prompt before it can cache the KV-state.

• Example:* in the GPT-4 Mini + Sonic-2 (EN) stack the first TTFT clocks at ≈ 1.22 s, but subsequent turns fall to ≈ 0.42–0.90 s. The “prompt tax” is ~300–800 ms, and it vanishes after turn 2 because the model re-uses its internal cache.

2. We’re Getting Closer to Human Latency — But Not Quite There Yet

• Human comfort band: ~0.1–0.4 s one-way; anything above 0.6–0.7 s starts to feel "robotic."
• Best first syllable today: 0.73 s (GPT-4 Nano + Sonic-Turbo, EN) and 0.75 s (same stack, ES). That’s about 2× slower than a natural gap, but already below the ITU’s 400 ms threshold for unacceptable RTT.
• Second turn latency: Since TTFT drops to 0.26–0.40 s and TTFB remains around 0.43 s, many loops land just under 0.7–0.8 s—close enough that most users don’t perceive a delay.

3. English Still Wins the Speed Race

Across the board, Spanish incurs an extra +300–500 ms in TTFT, and often a few additional milliseconds in TTFB.

This isn't surprising: most language models are trained on English-dominant datasets, and their tokenizers are optimized for English morphology. That means fewer tokens per word, higher-confidence predictions, and faster decoding paths.

In contrast, other languages often lead to:

• More tokens per sentence (due to suboptimal tokenization),
• Less frequent vocabulary (slower logits resolution),
• Slightly longer prompts (higher input load),
• And more uncertainty during generation (costlier decoding).

Model providers are still actively optimizing multilingual performance—but for now, English remains the latency benchmark.

4. STT Is a Non-Issue (When Streamed)

Deepgram’s streaming mode continually emits tokens, so by the time the user finishes speaking the transcript is already done. < 5 ms in our logs—effectively zero.

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Which Stack for Whom?

(Quality judgments are subjective; we used blind AB tests on 30 clips per stack.)

Conclusions & Near-Term Outlook

1. Composable beats monolithic—today.
   
   Because STT, LLM, and TTS evolve on different cadences, a modular pipeline lets you upgrade components the moment something faster drops—unlike monolithic models, where you must wait for the next provider release.

2. Sub-second voice loops are already viable for English and edging in for Spanish. With smarter caching, phoneme-level streaming, and incremental TTS we expect < 500 ms within a year.

3. Model shrinkage will continue.
   
   “Nano” and “flash” checkpoints show that aggressive distillation + quantization can keep quality “good enough” while halving latency every generation.

4. Edge deployment is accelerating.
   
   Thanks to aggressive quantization (8-bit and even 4-bit), large language and speech models are now deployable on local hardware—consumer GPUs, mobile NPUs, and even embedded systems. This allows parts of the voice loop to run on-device, cutting out network delays and shaving 50–150 ms off total latency.
   
   Source: “AI Voice Inference at the Edge is Finally Here,” VoiceTech Insights, 2025

5. Joint LLM-TTS training is emerging.
   
   A new generation of end-to-end speech models is beginning to bypass traditional TTS stages entirely. These models, like VITA-Audio (2025), predict multiple audio tokens in a single step, generating speech directly from text while drastically reducing inference time. Once stable in streaming mode, these architectures could cut TTS latency to mere milliseconds.
   
   Source: “VITA-Audio: Parallel Token-to-Audio Generation with Context-Aware Semantic Guidance,” arXiv, May 2025

Bottom line: We’re only a few iteration cycles away from voice agents that consistently reply in the same temporal rhythm as humans. If you build with LiveKit-style modular pipelines today, you can ride that curve with an overnight adjustment.

Stay tuned—the sub-half-second voice loop is closer than most teams think.