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Voice recording · Wake Word Detection

Voice Recording for AI Training for Wake Word Detection

Studio voice recording built for model training rather than broadcast: controlled acoustics, consistent mic distance, and reproducible session parameters across every speaker. Applied to wake word detection, the specification is driven by one thing: false accepts per hour.

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Smart speaker listening for a wake word in an Indian home — Voice Recording for AI Training for Wake Word Detection
Service
Voice recording
Use case
Wake Word Detection
Primary metric
False accepts per hour
01

Required data profile

  • Thousands of speakers, few utterances each
  • Positive and hard-negative sets
  • Multiple distances and noise conditions
Voice recording — Wake Word Detection · written into the SOW before recordingSample rate48 kHzBit depth24-bitMicrophoneLarge-diaphragm condenser, fixed distance, pop filterRoomTreated booth, RT60 under 0.3sProcessingNone. No compression, EQ, or noise reduction on delivered a…Your values replace ours rather than being converted after delivery.
02

Technical specification

ParameterStandard
Sample rate48 kHz
Bit depth24-bit
MicrophoneLarge-diaphragm condenser, fixed distance, pop filter
RoomTreated booth, RT60 under 0.3s
ProcessingNone. No compression, EQ, or noise reduction on delivered audio
Session length30-90 minutes per speaker, with breaks logged
Structured dataset packages ready for delivery — supporting voice recording for ai training for wake word detection
Structured dataset packages ready for delivery
03

Process

  • Session template definition so every studio in the network records identically
  • Speaker briefing and pronunciation calibration
  • Take-level monitoring with immediate re-record on defect
  • Per-session technical report
  • Delivery with unprocessed masters
04

Metrics this feeds

  • False accepts per hour
  • False reject rate per accent band
  • Performance at 3m and 5m
05

Failure modes to design out

  • Positives only, with no hard negatives
  • Close-mic-only capture
  • No accent-band tagging, so failures cannot be localised

Delivered audio is never processed. Training pipelines should see the raw capture so augmentation stays under your control.

06

Deliverables

  • Unprocessed WAV masters
  • Session logs
  • Take-level metadata
  • Speaker consent records

Frequently asked

Is voice recording the right service for wake word detection?

It covers thousands of speakers, few utterances each. Most wake word detection programmes combine it with at least one other service; we will say so in the scope rather than selling one line item.

What languages are available?

All 14 languages in the network plus Indian English accent bands.

How is the evaluation set handled?

Collected first, from speakers disjoint from the training cohort, so improvement is measurable.

Scope voice recording for wake word detection

Send the metric you need to move.

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