aidataservices.inAI data collection · India

Call-centre speech · Wake Word Detection

Call Centre Speech Data for Wake Word Detection

Simulated and consented real-world contact-centre audio in Indian languages, recorded over telephony-grade channels so it matches the bandwidth your production system actually sees. Applied to wake word detection, the specification is driven by one thing: false accepts per hour.

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Contact centre agents generating call centre speech data — Call Centre Speech Data for Wake Word Detection
Service
Call-centre speech
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
Call-centre speech — Wake Word Detection · written into the SOW before recordingChannel8 kHz narrowband telephony plus 48 kHz reference where requ…CodecG.711 / Opus simulated to match your stackStructureAgent and customer on separate channelsScenariosBilling, delivery, KYC, recharge, collections, support, sal…EmotionNeutral, frustrated and escalated variants on requestYour values replace ours rather than being converted after delivery.
02

Technical specification

ParameterStandard
Channel8 kHz narrowband telephony plus 48 kHz reference where required
CodecG.711 / Opus simulated to match your stack
StructureAgent and customer on separate channels
ScenariosBilling, delivery, KYC, recharge, collections, support, sales
EmotionNeutral, frustrated and escalated variants on request
Structured dataset packages ready for delivery — supporting call centre speech data for wake word detection
Structured dataset packages ready for delivery
03

Process

  • Scenario library built from your call taxonomy
  • Agent-side speakers briefed on your script and tone
  • Recording over the specified codec path
  • Transcription with intent and outcome labels
  • Delivery with per-scenario counts
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

Narrowband is simulated at capture time, not by downsampling clean studio audio, which would leave unrealistic artefacts.

06

Deliverables

  • Dual-channel call audio
  • Verbatim transcripts
  • Intent, outcome and sentiment labels
  • Scenario coverage matrix

Frequently asked

Is call-centre speech 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 call-centre speech for wake word detection

Send the metric you need to move.

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