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Overview

Voice Activity Detection (VAD) is a crucial component in speech processing systems. It is a signal processing technique used to detect the presence or absence of human speech in an audio signal. By differentiating between speech and non-speech segments, VAD optimizes the performance of real-time speech applications, including Automatic Speech Recognition (ASR), Voice over IP (VoIP), and conversational AI.

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Key Parameters in VAD

The following parameters are available for configuring VAD in your Real-Time Speech API:

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Turn Detection

  • Configuration: Turn detection is an optional feature that can be enabled or disabled. Setting it to null disables turn detection.
  • Server VAD: This is the currently supported type of turn detection. It detects the start and end of speech based on audio volume and responds at the end of the user’s speech.

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Parameters for Turn Detection

ParameterDescriptionDefault ValueRange
typeType of turn detection. Currently, only server_vad is supported.server_vadN/A
thresholdActivation threshold for VAD (0.0 to 1.0). Higher values require louder audio to activate.0.50.0 - 1.0
prefix_padding_msAmount of audio to include before the VAD-detected speech (in milliseconds).300msN/A
silence_duration_msDuration of silence to detect speech stop (in milliseconds). Shorter values improve response time.500msN/A
create_responseWhether to automatically generate a response when VAD is enabled.truetrue/false

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Explanation of Parameters

  • threshold: Adjust this value based on the noise level of the environment. A higher threshold is ideal for noisy settings, ensuring the model activates only for significant audio inputs.
  • prefix_padding_ms: Useful for capturing audio context before detected speech starts, providing smoother interactions.
  • silence_duration_ms: This parameter controls the responsiveness of the system. Lower values result in faster responses but may cut off speech during short pauses.
  • create_response: Enabling this ensures the system generates a response as soon as speech is detected and stops, streamlining interaction workflows.

By fine-tuning these parameters, users can optimize VAD performance for various real-time applications, ensuring precise and efficient voice interactions.