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TTS datasets · Wake Word Detection

TTS Training Data for Wake Word Detection

Single-speaker and multi-speaker text-to-speech corpora with phonetically balanced scripts, consistent prosody, and studio-grade capture suitable for neural TTS. Applied to wake word detection, the specification is driven by one thing: false accepts per hour.

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Studio-grade voice recording session for text-to-speech training data — TTS Training Data for Wake Word Detection
Service
TTS datasets
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
TTS datasets — Wake Word Detection · written into the SOW before recordingSample rate48 kHz, 24-bitSpeaker consistencySame booth, mic, distance and time-of-day banding across se…ScriptPhonetically balanced, diphone-covering, domain-extendedProsodyNeutral base set plus optional expressive stylesAlignmentText-audio alignment verified per utteranceYour values replace ours rather than being converted after delivery.
02

Technical specification

ParameterStandard
Sample rate48 kHz, 24-bit
Speaker consistencySame booth, mic, distance and time-of-day banding across sessions
ScriptPhonetically balanced, diphone-covering, domain-extended
ProsodyNeutral base set plus optional expressive styles
AlignmentText-audio alignment verified per utterance
SilenceLeading/trailing silence trimmed to a fixed window
Transcriber timestamping Indian language audio — supporting tts training data for wake word detection
Transcriber timestamping Indian language audio
03

Process

  • Script generation with phoneme and diphone coverage analysis
  • Voice casting with client shortlisting from audition samples
  • Multi-session recording with drift monitoring between sessions
  • Alignment verification and mispronunciation review by a linguist
  • Delivery with a coverage 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

Session drift is the main TTS killer. Every session is compared acoustically against the reference session and re-recorded if it drifts.

06

Deliverables

  • Studio WAV per utterance
  • Verified transcripts and pronunciation notes
  • Phoneme coverage report
  • Voice talent licence and consent documentation

Frequently asked

Is tts datasets 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 tts datasets for wake word detection

Send the metric you need to move.

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