Speech AI Companies · ASR Model Training
ASR Model Training 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. Building or fine-tuning speech recognition for Indian languages from scratch or from a multilingual base model.
- Buyer
- Speech AI Companies
- Use case
- ASR Model Training
- Metric
- Word error rate overall and per dialect
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 asr model training problem, and it is solved by data shaped like this:
- Hundreds to thousands of hours of verbatim-transcribed speech
- Wide speaker diversity: age, gender, region, education, recording condition
- Speaker-disjoint train/dev/test splits
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
- Word error rate overall and per dialect
- Entity error rate on names and numbers
- Code-switch token accuracy
Pitfalls
- Read-speech-only corpora that do not transfer to spontaneous audio
- Speaker leakage across splits inflating reported accuracy
- Normalised-only transcripts with the raw text discarded
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.