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Translation · Wake Word Detection

Translation & Localisation Data for Wake Word Detection

Human translation and parallel-corpus creation across Indian languages, built for machine-translation training and multilingual LLM evaluation rather than for publication. Applied to wake word detection, the specification is driven by one thing: false accepts per hour.

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Abstract visualisation of translation between two Indian languages — Translation & Localisation Data for Wake Word Detection
Service
Translation
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
Translation — Wake Word Detection · written into the SOW before recordingDirectionEnglish to Indian languages and between Indian languagesOutputSentence-aligned parallel corporaRegisterFormal, colloquial, or matched to your product toneReviewIndependent bilingual review passTerminologyClient glossary enforced and returned updatedYour values replace ours rather than being converted after delivery.
02

Technical specification

ParameterStandard
DirectionEnglish to Indian languages and between Indian languages
OutputSentence-aligned parallel corpora
RegisterFormal, colloquial, or matched to your product tone
ReviewIndependent bilingual review pass
TerminologyClient glossary enforced and returned updated
Diverse Indian speakers waiting for multilingual data collection sessions — supporting translation & localisation data for wake word detection
Diverse Indian speakers waiting for multilingual data collection sessions
03

Process

  • Glossary and register agreement
  • Translation by native speakers of the target language
  • Independent bilingual review
  • Alignment verification
  • Delivery with terminology report
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

Back-translation checks are run on a sample so you can see where source ambiguity, not translator error, causes divergence.

06

Deliverables

  • Sentence-aligned parallel data
  • Updated glossary
  • Reviewer notes on ambiguous source

Frequently asked

Is translation 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 translation for wake word detection

Send the metric you need to move.

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