Speech AI Companies · TTS Voice Building
TTS Voice Building Data for Speech AI Companies
Teams whose core product is speech recognition or synthesis, where dataset quality is the product roadmap and word error rate is the metric everyone watches. Creating a natural synthetic voice in an Indian language, from casting through to a trainable studio corpus.
- Buyer
- Speech AI Companies
- Use case
- TTS Voice Building
- Metric
- MOS naturalness
Where the two meet
WER on Indian languages is dominated by dialect and code-mixing failures that generic corpora do not cover That is a tts voice building problem, and it is solved by data shaped like this:
- 10-40 hours from one speaker, or multi-speaker sets
- Phonetically balanced scripts
- Session-consistent acoustics
Your evaluation criteria
- Are train/dev/test splits speaker-disjoint by construction?
- Is transcription verbatim, with disfluencies preserved?
- Is per-token language ID available for code-mixed speech?
- Is inter-annotator agreement measured and reported?
Metrics
- MOS naturalness
- Pronunciation accuracy on loanwords and names
- Prosody stability across long utterances
Pitfalls
- Session drift between recording days
- Scripts that under-cover rare phonemes
- Uncleared voice-talent licensing
Contract points
- Speaker-disjoint splits guaranteed contractually
- Right to publish benchmark results
- Re-record remedy for QA failures
Frequently asked
What does a first engagement look like?
Usually a scoped pilot: one language, an evaluation set plus a first training batch, delivered in three to five weeks, followed by the full programme.
Can you match our existing vendor's schema?
Yes. Working to your schema avoids a conversion pass and keeps deliveries comparable across vendors.
How is provenance documented?
Per-item contributor records and consent mapped to IDs in the manifest.
Send your requirement
Language, volume, metric, deadline.