Handling barge-in : What Happens When Users Interrupt Your AI mid-sentence

If you've ever built a voice-enabled application, you know that real human conversations are nothing like the neat request-response flow we designed for: User interrupt. They talk over your AI. They change their mind at mid-sentence. And if your system can't handle that, you will have frustrated users very quickly.

The ability to handle interruptions, commonly called "Barge-in" in the voice AI world, is what separates robotic IVR systems from actual conversational AI experiences.

I've been dealing with real-time audio processing for a while now and can tell you that Barge-in handling is one of those features that sounds simple on paper but becomes really complicated when you start implementing it. Let me walk you through what is actually happening under the hood and how to approach this problem.

Why Barge-In matters

Think about how you talk with another human - you don't wait for them to finish every single word before responding - you anticipate, you react - sometimes you kill them because you already know what they are going to say - that's natural conversation

Imagine you are calling the bank's automated system and having to listen to the first time -- "For account balance, press 1. For recent transactions, press 2. For speaking with a representative..." -- without being able to say "representative" the moment you hear the first option -- that's the frustration we're trying to eliminate.

The term barge-in actually comes from telephony systems back in the 1980s, when Bell Labs and others started experimenting with allowing callers to ignore voice prompts. What started as a simple feature to skip IVR menus has evolved into a fundamental requirement for any conversational AI that wants to feel natural.

The technical challenge

Here's the thing about Barge-in - Implementation: Your system has to do something that had historically never been designed for the voice systems, it needs to talk and listen at the same time - This is called full-duplex audio processing and is harder than it sounds.

When your AI is speaking you have audio / audio out, but also you need to monitor the incoming audio / audio / user speech for instance . And here's the tricky part, you need to distinguish between :

1. The user actually interrupts with meaningful input.
2. Background noise (TV, traffic, other people talking)
3. The user making filler sounds (Uh-huh, "mm", "right") that do not require interruptions
4. A voice of your own AI returning the echo.

Let me show you what this looks like in a basic implementation:

class BargeInHandler {
  constructor(config = {}) {
    this.vadThreshold = config.vadThreshold || 0.7;
    this.noInterruptTime = config.noInterruptTime || 1000; // ms
    this.isAISpeaking = false;
    this.speechStartTime = null;
  }

  onAudioFrame(audioBuffer, timestamp) {
    // Skip if we're in the no-interrupt window
    if (this.isAISpeaking &&
        timestamp - this.speechStartTime < this.noInterruptTime) {
      return { shouldInterrupt: false };
    }

    const vadScore = this.detectVoiceActivity(audioBuffer);

    if (vadScore > this.vadThreshold) {
      return {
        shouldInterrupt: true,
        confidence: vadScore,
        audio: audioBuffer
      };
    }

    return { shouldInterrupt: false };
  }

  detectVoiceActivity(audioBuffer) {
    // This is where VAD magic happens
    // Usually using energy levels, zero-crossing rates,
    // or neural network based detection
  }
}

This is obviously simplified but shows the basic flow and the real complexity comes when you start bringing up edge cases.

Voice Activity Detection (VAD)

The foundation of any barge-in system is voice activity detection - VAD is what answers the simple question "Is someone speaking right now?"

Traditional VAD systems use signal processing techniques like energy thresholds and zero-crossing rates that work OK in quiet environments but fall apart when there is background noise. Modern systems use neural networks trained on thousands of hours of audio to distinguish speech from everything else.

# Example using Silero VAD (one of the best open-source options)
import torch

model, utils = torch.hub.load(
    repo_or_dir='snakers4/silero-vad',
    model='silero_vad'
)

def check_speech(audio_chunk):
    """
    Returns probability of speech in audio chunk
    audio_chunk should be 16kHz, mono
    """
    speech_prob = model(
        torch.tensor(audio_chunk),
        16000
    ).item()

    return speech_prob > 0.5

The key metrics you care about for VAD in barge-in scenarios are:

• Latency: How quickly do you detect speech? For natural conversations, you need sub-100ms detection.
• ** False Positive Rate**: How often do you interrupt in non-speech sounds? Too high and your AI will stop for each car horn mid-sentence.
• False Negative Rate* : How often do you miss actual speech? Too high and users feel ignored.

There's always a trade-off between these: a more sensitive VAD gets more interruptions but also more false positives.

Handling the Interruption

So you've detected that the user is speaking, now what? You have several options :

Option 1: Immediate Stop

The simplest approach is to immediately stop the AI output as soon as you detect speech – this feels responsive but may be jarring if the user had just made a filler sound.

bargeInHandler.on('speech-detected', () => {
  audioOutput.stop();
  transcriber.startCapture();
});

Option 2: Phrase-Level Interruption

Wait for the AI to finish its current phrase before stopping, this feels more natural but adds latency.

bargeInHandler.on('speech-detected', () => {
  audioOutput.finishCurrentPhrase();
  transcriber.startCapture();
});

Option 3: Conditional Barge In

For example, only interrupt for certain types of input; ignore "uh-huh" and "ok" but interrupt for actual questions or commands.

bargeInHandler.on('speech-detected', async (audio) => {
  const transcript = await transcriber.quickTranscribe(audio);

  // Check if this is meaningful input
  if (isBackchannel(transcript)) {
    // User is just acknowledging, don't interrupt
    return;
  }

  audioOutput.stop();
  processUserInput(transcript);
});

This third option is what most production systems use because it provides the best user experience BUT it requires more sophisticated processing since you need to understand what the user is saying before deciding to interrupt it.

State management after corruption

Here's something that many tutorials skip over - what happens to your conversation state when the user interrupts?

Consider this scenario : Your AI is explaining a three-step process and gets interrupted in the middle of step 2 and the user asks a clarifying question after it answers the question:

1. Start over with step 1?
2. Continue from where it was interrupted?
3. Ask the user where they want to continue?

There's no one answer to this, it depends on your application, but you need to track where you were in the conversation and make intelligent decisions about how to proceed.

class ConversationState {
  constructor() {
    this.currentContext = null;
    this.interruptionStack = [];
  }

  onInterrupt(userInput, interruptPoint) {
    // Save where we were
    this.interruptionStack.push({
      context: this.currentContext,
      position: interruptPoint,
      timestamp: Date.now()
    });

    // Process the interruption
    return this.handleInterruptedInput(userInput);
  }

  shouldResumeAfterInterrupt() {
    const lastInterrupt = this.interruptionStack[this.interruptionStack.length - 1];

    // Check if the interruption was a clarification
    // vs a complete topic change
    return lastInterrupt?.wasRelatedQuestion;
  }
}

The Echo Cancellation Problem

One thing that will bite you if you're not careful is echo : When your AI speaks through a speaker , that audio can be captured by the microphone and interpreted as user speech . Without proper echo cancellation, your AI will always interrupt itself.

Most real-time audio systems include the Acoustic Echo Cancellation (AEC) as part of their audio processing pipeline . If you are building on top of WebRTC or similar technologies you get this for free . If you're building something custom you need to implement it.

The basic idea is to keep a reference of what audio you are sending and subtract that signal (with appropriate delay and filtering) from the incoming audio before doing VAD.

// Simplified echo cancellation concept
class EchoCanceller {
  constructor() {
    this.outputBuffer = new RingBuffer(16000 * 2); // 2 seconds at 16kHz
  }

  onAudioOutput(samples) {
    this.outputBuffer.write(samples);
  }

  processInput(inputSamples) {
    const echoReference = this.outputBuffer.read(inputSamples.length);

    // In reality, this is match more complex
    // involving adaptive filtering and delay estimation
    return subtractEcho(inputSamples, echoReference);
  }
}

Best practices

After implementing barge-in in several systems, here's what I've learned:

Don't allow an interruption immediately. Give your AI at least 500ms-1000ms before allowing interruption, this prevents accidental interruptions from left user speech or system sounds.

Design interruptible content. Structure your AI responses so they can be interrupted at natural break points . Long monologues are bad for conversation flow anyways.

Keep critical information uninterruptible. If your AI is giving important information (like confirmation numbers, legal disclaimers, etc.) , you may want to disable barge-in for those specific utterances.

Track the interruption patterns. If users continually interrupt at certain points, that's valuable feedback about your conversation design.

Test with real noise. Your system will work perfect in a quiet room. Test it with TV in the background, in a car, in a coffee shop... It's where VAD systems fail.

Conclusion

Barge-in handling is one of those features that separates demo-quality voice AI from production-ready systems and requires careful coordination between VAD, audio processing, state management and conversation design.

The good news is that the building blocks are getting better every year : Modern VAD systems like Silero can run in real-time on almost any hardware ; real-time audio frameworks handle the complexities of full dual streaming ; and LLMs are getting better at understanding interrupted contexts.

The bad news is that there is no single library that does all of this for you - you must understand the components and wire them together in a way that works for your specific use case.

If you are building voice AI and are struggling with natural conversation flow, start with getting the VAD right , all else depends on accurately detecting when users are speaking, and once you have that, the rest is just software engineering.

If you found this helpful, I'd love to hear about your experiences in barge-in : What challenges have you faced? What solutions have worked for you?

Thanks for reading!