Locale & accent plans
Explicit coverage tables by market. We document demographic targets instead of vague multilingual claims.
Voice products depend on microphone geometry, room noise, and who was recorded. Studio demos alone rarely match what ships in customer hands. We plan corpora for your devices and markets.
Speech AI training data collection records or licenses audio with documented consent, transcribes and labels it (words, speakers, intent, emotion where needed), normalizes formats, and removes PII so ASR, TTS, and dialog models train on realistic, rights-cleared speech.
Enterprise buyers ask for locale matrices, consent artifacts, and word-error-rate gains on their hardware. RLHF and instruction tuning for voice agents also need preference pairs tied to your brand safety rules.
| Dataset type | Typical output | Quality lever |
|---|---|---|
| Read speech | Phonetically balanced prompts | Multiple takes, SNR metadata |
| Conversational | Multi-speaker dialog | Diarization QA, overlap handling |
| Device-specific | Far-field on your product | Matched mic placement and DSP chain |
| Call center | Redacted telephony | PCI/PII scrub, domain vocab |
Pair collection with edge AI and AIoT hardware when wake-word and on-device ASR must match production acoustics.
Explicit coverage tables by market. We document demographic targets instead of vague multilingual claims.
Participant agreements, voice-print handling, and license scope for training versus TTS cloning.
Word-level review, OOV handling, and WER measurement on held-out gold clips.
FLAC/WAV/JSON manifests, PII redaction in transcripts, and versioned drops to your bucket.
Both can work with proper consent and QA. Enterprise deployments increasingly require provenance and voice-rights clarity. Licensed or first-party recorded corpora reduce IP and publicity risk compared with scraped audio.
Yes. Recording on your prototype is often the fastest path to production WER because mic spacing, enclosure, and DSP match what customers use.
We structure preference and safety labels against your policy docs, with evaluator training and agreement metrics.