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Voice Activity Detection (VAD) automatically identifies when human speech is present in an audio stream, distinguishing it from background noise, silence, or other non-speech sounds. It allows applications to process audio more efficiently by focusing computational resources only on segments that contain actual speech, improving both performance and accuracy of voice-based features.

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How does voice activity detection work?

Here’s what happens when audio flows through a VAD system:
  1. Audio Input: Raw audio data enters the VAD system for processing.
  2. AI VAD Model: The audio is processed through an AI model that contains the core VAD engine.
  3. AI VAD Engine: This engine performs three key functions:
    • Audio Understanding: Analyzes the audio stream for patterns and characteristics that indicate human speech
    • Pattern Recognition: Uses neural processing to distinguish human speech from background noise, music, or silence
    • Decision Making: Makes real-time decisions about whether speech is present, often with configurable sensitivity levels
  4. Speech Detection: The final output indicates whether speech has been detected or not.
VAD Basics

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How does it work with Stream?

With Vision Agents you get a plugin system that handles all the detection logic for you. Here’s how it works in your calls:
  1. Call Audio: Audio from your Stream video call enters the system.
  2. VAD Plugin: The VAD plugin processes the incoming audio through the AI processing pipeline.
  3. AI analysis: The VAD analyses the audio to classify the type of sound - whether it’s speech or just noise. It emits partial events at intervals while it’s analysing audio.
  4. Your Application: Your application receives both the analysed audio and its categorisation to respond appropriately. E.g. starting transcription, managing conversation flow, or optimizing performance.
VAD with Stream

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Worked example

Let’s walk through a real-world scenario to see how VAD transforms your application’s intelligence. Imagine you’re building a smart meeting assistant that automatically transcribes conversations. Here’s how VAD makes this system work efficiently: The Scenario: A team meeting with 6 participants in a room with some background noise. What Happens:
  1. The VAD system continuously monitors all audio from the call, analyzing patterns in real-time.
  2. When someone starts speaking, VAD immediately detects the speech and triggers an “audio” event.
  3. Your application receives this event and starts the transcription process only for that speaker, saving resources by not processing silence or background noise.
  4. Your application can then finalize the transcription for that segment and prepare for the next speaker.
  5. Throughout the meeting, VAD helps manage conversation flow by identifying when different people are speaking, enabling features like speaker identification and turn management.

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Voice Activity Detection vs Turn Detection

Voice Activity Detection (VAD) and turn detection solve different problems:
  • VAD answers: “Is someone speaking right now?”
  • Turn Detection answers: “Has the speaker finished their turn?”
VAD detects when speech starts and stops. Turn detection determines when it’s appropriate to respond. Using VAD alone often causes interruptions during natural pauses or awkward delays after speech ends. Turn detection adds conversational intelligence on top of VAD. For voice AI applications that need to respond to users (like voice assistants or customer service bots), combining VAD with turn detection creates more natural interactions. VAD identifies speech segments, and turn detection determines the right moment to respond.

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Applications of Voice Activity Detection

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Intelligent Transcription

Only transcribe audio when someone is actually speaking, saving processing power and improving accuracy. Create more efficient transcription systems that focus on meaningful content.

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Conversation Management

Detect when different people are speaking to manage turn-taking, identify speakers, or create more natural conversation flows in your applications.

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Audio Processing Optimization

Save computational resources by only processing audio during speech segments. Reduce costs and improve performance in applications that handle large amounts of audio data.

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Noise Reduction and Filtering

Automatically filter out background noise, music, or other non-speech audio to improve the quality of your audio processing pipeline.

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Real-time Analytics

Track speaking patterns, measure participation levels, or analyze conversation dynamics in meetings, calls, or interactive sessions.

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Accessibility Features

Create applications that respond to voice commands more reliably by only listening when someone is actually speaking, reducing false triggers and improving user experience.

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Recording and Archiving

Automatically start and stop recordings based on speech activity, creating cleaner audio files that focus on actual conversation content.
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