> ## Documentation Index
> Fetch the complete documentation index at: https://assemblyai.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Optimizing Accuracy and Latency
> Tune streaming models to balance transcription accuracy and latency.
## Mode
Universal-3.5 Pro Streaming workloads sit on a spectrum between two competing goals: returning transcripts as fast as possible, and returning the most accurate transcripts possible. To make this tradeoff explicit, Universal-3.5 Pro supports a **mode** connection parameter you can set when opening a streaming session.
| Mode | Value | When to use |
| ------------------ | -------------- | ------------------------------------------------------------------------------------------------------------------------------ |
| Min latency | `min_latency` | Lowest possible time-to-text. Best when responsiveness matters more than catching every word. |
| Balanced (default) | `balanced` | A middle ground between latency and accuracy. Best for voice agents and other interactive applications. |
| Max accuracy | `max_accuracy` | Highest transcription accuracy. Best for note-taking, scribes, and post-call analysis where a small added delay is acceptable. |
Set the `mode` connection parameter when you open the WebSocket.
```python theme={null}
CONNECTION_PARAMS = {
"sample_rate": 16000,
"speech_model": "universal-3-5-pro",
"mode": "balanced", # min_latency | max_accuracy
}
```
```python theme={null}
client.connect(
StreamingParameters(
sample_rate=16000,
speech_model="universal-3-5-pro",
mode="balanced", # min_latency | max_accuracy
)
)
```
```javascript theme={null}
const CONNECTION_PARAMS = {
sample_rate: 16000,
speech_model: "universal-3-5-pro",
mode: "balanced", // min_latency | max_accuracy
};
```
```javascript theme={null}
const transcriber = client.streaming.transcriber({
sampleRate: 16_000,
speechModel: "universal-3-5-pro",
mode: "balanced", // min_latency | max_accuracy
});
```
## Language selection
By default, Universal-3.5 Pro Streaming is multilingual and code-switches natively across all supported languages with no configuration. When you know which languages a session will use, pass the `language_codes` connection parameter to steer the model toward them and improve language accuracy.
* For a **known subset**, pass the codes you expect (for example, `["en", "es"]`). The model still code-switches, but heavily biased to the languages you list.
* For a **monolingual session**, pass a single-element list (for example, `["es"]`).
* For **full multilingual**, omit `language_codes` to keep native code switching.
See [Multilingual transcription](/docs/streaming/multilingual-transcription) for the full feature guide, including supported languages, language detection, and updating `language_codes` mid-stream.
## Turn detection
Universal-3.5 Pro Streaming detects the end of a turn using the transcript rather than silence alone. The `mode` preset sets the defaults, and every turn detection parameter can be overridden on the connection or updated mid-stream to tune endpointing for your use case.
* Raise `min_turn_silence` when brief pauses end turns too early, for example while a caller dictates a phone number. Raise `max_turn_silence` when you expect longer pauses within a turn.
* Lower `vad_threshold` when quiet speech is missed. When background noise causes false interruptions, raise `vad_threshold`, increase `interruption_delay`, or enable [Voice Focus](/docs/streaming/voice-focus).
See [Turn detection](/docs/streaming/turn-detection) for the full feature guide, including the turn lifecycle, entity capture, and bringing your own turn detection.
Universal Streaming uses **confidence-based turn detection**. The model predicts when speech naturally ends; if confidence exceeds `end_of_turn_confidence_threshold` and `min_turn_silence` has passed, the turn ends. Acoustic (silence-based) detection kicks in as a fallback after `max_turn_silence`.
| Parameter | Default | Description |
| ---------------------------------- | --------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| `end_of_turn_confidence_threshold` | `0.4` | Confidence threshold for semantic end-of-turn. Higher = more confident before ending; lower = ends faster. |
| `min_turn_silence` | `400` ms | Silence required before a semantic end-of-turn fires. |
| `max_turn_silence` | `1280` ms | Maximum silence before forcing a turn to end via acoustic detection. |
| `vad_threshold` | `0.4` | Confidence threshold (0 to 1) for classifying audio frames as speech. Increase for noisy environments to reduce false speech detection. |
**Quick-start configurations**
Aggressive, for short, rapid back-and-forth (IVR replacements, order confirmations).
```javascript theme={null}
const streamingConfig = {
end_of_turn_confidence_threshold: 0.4,
min_turn_silence: 160,
max_turn_silence: 400,
};
```
Balanced, for most conversational voice agents (customer support).
```javascript theme={null}
const streamingConfig = {
end_of_turn_confidence_threshold: 0.4,
min_turn_silence: 400,
max_turn_silence: 1280,
};
```
Conservative, for reflective or complex speech (healthcare, sales, legal).
```javascript theme={null}
const streamingConfig = {
end_of_turn_confidence_threshold: 0.7,
min_turn_silence: 800,
max_turn_silence: 3600,
};
```
**Disabling turn detection**
If you're using your own VAD or turn detection model, send a `ForceEndpoint` event to force a turn boundary:
```python theme={null}
ws.send(json.dumps({"type": "ForceEndpoint"}))
```
Or set `end_of_turn_confidence_threshold` to `1` (acoustic-only fallback) or `0` (silence-only). Setting it to `0` is **not recommended** unless you have a custom turn detection model running on top, because it forces a turn at every `min_turn_silence`-length pause and fragments mid-sentence thinking pauses.