Echo TurnDetection
Collection
Echo is a series of End-of-Turn models built to make voice agents respond at exactly the right moment. • 3 items • Updated • 1
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Echo Small is the fastest way to stop your agent talking over people.
A verdict in under 10 ms, decided before the silence even registers.
Most turn detectors wait for silence and hope. A 500 ms gap looks identical whether someone finished a sentence, paused to think, or simply took a breath, so agents built on timers end up talking over people or leaving them hanging.
Echo Small does not guess. It reads the semantic meaning of what the user just said and decides whether the thought closed or trailed off mid-way. One prediction per turn, with a confidence score, returned before a silence timer would have finished counting.
It is the quick one in the Echo family and the default for most agents: 12 languages, four conversational states, and the lowest latency of the three. It also beats the proprietary turn detectors most voice stacks ship with.
🎯 What it does
Echo Small classifies every user turn into one of four states. Each state tells the agent exactly what to do next.
| State | What it means | What your agent should do |
|---|---|---|
| Complete | The user has finished their turn | Hand the turn to the LLM and respond |
| Incomplete | The user is mid-sentence, just pausing | Keep listening. Do not take the floor |
| Backchannel | A short acknowledgement: "uh-huh", "okay okay" | Keep speaking. This is not an interruption |
| Wait | The user is asking you to hold: "wait a minute", "hold on" | Stop speaking immediately |
Most turn detectors only answer the first two. Backchannel and Wait are the states that make an agent feel polite instead of oblivious: not stopping every time someone says "mm-hm", and stopping the instant someone says "hold on".
🔌 Input and output
Echo Small works from semantic understanding alone.
Input, per turn:
| Field | Description |
|---|---|
| Utterance | What the user just said |
No language tag required. Echo detects the language itself, so there is nothing to configure per call.
Output, one prediction per turn:
| Field | Description |
|---|---|
| State | Complete, Incomplete, Backchannel or Wait |
| Confidence | A score for the prediction, so you can tune how decisive your agent is |
No audio needed and no language to declare. It drops into any pipeline as-is, and that is why it answers fastest and costs least to run.
🌍 Supported languages
12 languages, spanning Indian and European language families. Detected automatically, so you never pass a language code.
| Code | Language | Code | Language | Code | Language |
|---|---|---|---|---|---|
bn |
🇧🇩 Bengali | de |
🇩🇪 German | en |
🇺🇸 English |
es |
🇪🇸 Spanish | fr |
🇫🇷 French | gu |
🇮🇳 Gujarati |
hi |
🇮🇳 Hindi | it |
🇮🇹 Italian | mr |
🇮🇳 Marathi |
ta |
🇮🇳 Tamil | te |
🇮🇳 Telugu | ur |
Urdu |
This is not a model that works well in English and degrades everywhere else. Accuracy holds across the full set.
📊 Performance
Benchmarked on TURNS2K, 2,000 English conversational samples labelled Complete or Incomplete, against a leading third-party turn detector shown here as Baseline.
| Metric | Echo Small | Baseline |
|---|---|---|
| Accuracy | 93.60% | 61.13% |
| Recall (Complete) | 97.31% | 32.83% |
| Specificity | 88.91% | 96.83% |
| F1 Score (Complete) | 0.9443 | 0.4851 |
Echo Small beats the proprietary baseline by 32 points of accuracy and nearly 3x the recall. Recall is the number to watch. 97.31% of finished turns are recognised as finished, so the agent replies instead of leaving the user hanging. For every 100 times a user actually finished speaking, Echo Small responds 97 times. The baseline responds 33.
Results are measured on the benchmark described above. Performance may vary depending on language, deployment configuration, user behaviour and application requirements.
🧠 Trained on data built for this problem
Echo Small is trained on a proprietary, closed-source dataset built in-house, purpose-made for conversational turn-taking across all 12 languages and all four states.
That dataset is the reason the rare states hold up. Backchannels and hold requests barely appear in off-the-shelf corpora, so a model trained on public data has almost nothing to learn them from.
🎛️ The Echo family
All three models return the same four states, so you can move between them without changing your agent logic.
| Model | Modality | Latency | Best for |
|---|---|---|---|
echo-small |
Semantic | 5 to 10 ms | The lowest latency. The default when responsiveness matters most |
echo-large |
Semantic | 10 to 20 ms | Higher accuracy, when it matters more than raw speed |
echo-omni |
Audio + semantic | 60 to 80 ms | The widest language coverage, with acoustic understanding on top of the semantics |
🚀 Get access
Echo Small is served for you. There is nothing to download, host, or keep running. Plug it straight into your voice pipeline alongside your existing STT, LLM and TTS.
# Set ZERORUNTIME_AUTH_TOKEN in your environment.
from zeroruntime.inference import TurnDetector
# Lowest latency, the default choice
turn_detector = TurnDetector(model="echo-small")
Echo Small by Zero Runtime
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