# Source: https://docs.pipecat.ai/guides/fundamentals/user-input-muting.md

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pipecat.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# User Input Muting

> Learn how to control when user speech is processed in your conversational bot

## Overview

In conversational applications, there are moments when you don't want to process user speech, such as during bot introductions or while executing function calls. Pipecat's user mute strategies let you selectively "mute" user input based on different conversation states.

## When to Use Mute Strategies

Common scenarios for muting user input include:

* **During introductions**: Prevent the bot from being interrupted during its initial greeting
* **While processing functions**: Block input while the bot is retrieving external data
* **During bot speech**: Reduce false transcriptions while the bot is speaking
* **For guided conversations**: Create more structured interactions with clear turn-taking

## How It Works

User mute strategies work by blocking specific user-related frames from flowing through your pipeline. When muted, the following frames are filtered:

* Voice activity detection (VAD) events
* Interruption signals
* Raw audio input frames
* Transcription frames (both interim and final)

This prevents user speech from being processed during muted periods.

<Note>
  Mute strategies are configured on the `LLMUserAggregator` via the
  `user_mute_strategies` parameter.
</Note>

## Mute Strategies

Pipecat provides several built-in strategies for determining when to mute user input:

<CardGroup cols={2}>
  <Card title="FirstSpeechUserMuteStrategy" icon="microphone-slash" iconType="duotone">
    Mute only during the bot's first speech utterance. Useful for introductions
    when you want the bot to complete its greeting before the user can speak.
  </Card>

  <Card title="MuteUntilFirstBotCompleteUserMuteStrategy" icon="hourglass-half" iconType="duotone">
    Start muted and remain muted until the first bot utterance completes.
    Ensures the bot's initial instructions are fully delivered.
  </Card>

  <Card title="FunctionCallUserMuteStrategy" icon="gear" iconType="duotone">
    Mute during function calls. Prevents users from speaking while the bot is
    processing external data requests.
  </Card>

  <Card title="AlwaysUserMuteStrategy" icon="volume-xmark" iconType="duotone">
    Mute whenever the bot is speaking. Creates a strict turn-taking conversation
    pattern.
  </Card>
</CardGroup>

<Warning>
  The `FirstSpeechUserMuteStrategy` and
  `MuteUntilFirstBotCompleteUserMuteStrategy` strategies should not be used
  together as they handle the first bot speech differently.
</Warning>

## Basic Implementation

Import and configure the mute strategies you need:

```python  theme={null}
from pipecat.processors.aggregators.llm_response_universal import (
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
)
from pipecat.turns.user_mute import AlwaysUserMuteStrategy

# Configure with one or more strategies
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
    context,
    user_params=LLMUserAggregatorParams(
        user_mute_strategies=[AlwaysUserMuteStrategy()],
    ),
)
```

## Combining Multiple Strategies

Multiple strategies can be combined. They use OR logic—if **any** strategy indicates the user should be muted, input is suppressed:

```python  theme={null}
from pipecat.processors.aggregators.llm_response_universal import (
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
)

from pipecat.turns.user_mute import (
    MuteUntilFirstBotCompleteUserMuteStrategy,
    FunctionCallUserMuteStrategy,
)

user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
    context,
    user_params=LLMUserAggregatorParams(
        user_mute_strategies=[
            MuteUntilFirstBotCompleteUserMuteStrategy(),  # Mute until first response
            FunctionCallUserMuteStrategy(),               # Mute during function calls
        ],
    ),
)
```

## Responding to Mute Events

You can register event handlers to be notified when muting starts or stops. This is particularly useful for providing visual feedback to users:

```python  theme={null}
@user_aggregator.event_handler("on_user_mute_started")
async def on_user_mute_started(aggregator):
    logger.info("User mute started")
    # Send a visual indicator to your client
    # e.g., show a "Bot is speaking" indicator

@user_aggregator.event_handler("on_user_mute_stopped")
async def on_user_mute_stopped(aggregator):
    logger.info("User mute stopped")
    # Update your client UI
    # e.g., show a "You can speak now" indicator
```

These events fire whenever the mute state changes, allowing you to keep your UI synchronized with the bot's state.

## Best Practices

* **Choose strategies wisely**: Select the minimal set of strategies needed for your use case
* **Test user experience**: Excessive muting can frustrate users; balance control with usability
* **Provide feedback**: Use the mute event handlers to show visual cues when the user is muted to improve the experience

## Next Steps

<CardGroup cols={2}>
  <Card title="User Mute Strategies Reference" icon="book" iconType="duotone" href="/server/utilities/turn-management/user-mute-strategies">
    Read the complete API reference documentation for all available mute
    strategies and their behavior.
  </Card>

  <Card title="User Turn Strategies" icon="arrows-turn-to-dots" iconType="duotone" href="/server/utilities/turn-management/user-turn-strategies">
    Learn how to configure turn detection behavior for more control over
    conversation flow.
  </Card>
</CardGroup>

Experiment with different muting strategies to find the right balance for your application.