Conversational AI Companies · Hindi
Hindi Training Data for Conversational AI Companies
Voice-bot and chat-plus-voice platforms deploying into Indian markets, where the gap between demo accuracy and live accuracy is a code-mixing problem. For Hindi specifically, the work is shaped by 7 dialect varieties and by how much English enters the speech.
- Buyer profile
- Conversational AI Companies
- Language
- Hindi (hi-IN)
- Typical ask
- 100-400 hours of scenario-driven conversational and telephony audio per language.
Your problem
- Bots trained on clean single-language data fail on real switching mid-utterance
- Barge-in, overlap and background noise are absent from scripted training data
- Intent coverage does not match the messy way Indian users actually phrase requests
What Hindi requires
- Four-way stop contrast (voiced/voiceless x aspirated/unaspirated) that collapses in models trained on English-first acoustic units
- Dialects: Khari Boli, Awadhi, Braj, Bhojpuri-influenced Hindi
- Urban Hindi speech is Hinglish in practice. Expect 15-40% English tokens in spontaneous speech: numbers, brands, technology terms, and whole clause switches. Any Hindi corpus that excludes English tokens will not match production traffic.
- Public Hindi corpora skew heavily towards read newspaper text from educated urban speakers in Delhi and NCR. Rural Bihar and eastern UP speech, elderly speakers, and low-literacy speakers reading prompts aloud are largely absent.
How you will evaluate the delivery
- Does the data include overlap, interruptions and backchannels?
- Are utterances collected over the same channel conditions as production?
- Is intent labelling done against your live taxonomy?
Recommended cohort
Largest recruitment pool in the network. A 1,000-speaker Hindi cohort with balanced gender and 18-45 age bands is typically fielded across four cities to avoid a single-city accent bias.
| Dimension | Typical split | Why it matters for Hindi |
|---|---|---|
| Gender | 50 / 50 | Pitch range differences change acoustic model behaviour; unbalanced cohorts bias recognition |
| Age | 18-25: 30%, 26-40: 40%, 41-60: 30% | Older speakers retain conservative Hindi forms that younger urban speakers have lost |
| Region | Uttar Pradesh / Bihar / Madhya Pradesh and others | Dialect spread across 7 recognised varieties |
| Education | Mixed, including below-graduate | Prompt-reading fluency correlates with education and skews prosody |
| Condition | Studio / quiet room / field | Match the noise profile of your deployment |
Contract points
- Scenario confidentiality
- Right to reuse across bot versions
- Delivery in a format that drops into an existing pipeline
Example requirement
"We need 2,000 hours of Hindi from 3,000 speakers, 50/50 male-female, ages 18-45, studio quality, scripted plus spontaneous, delivered in WAV with transcripts."
That sentence is enough to produce a quote and a timeline. Anything missing, we will ask about once.
Frequently asked
Do you have Hindi capacity available now?
Largest recruitment pool in the network. A 1,000-speaker Hindi cohort with balanced gender and 18-45 age bands is typically fielded across four cities to avoid a single-city accent bias. Fielding usually starts one to two weeks after the specification is signed.
Can you work white-label?
Yes, including QA reporting written so it can be passed to your end client unchanged.
What licensing applies to Hindi data?
Perpetual and transferable, with participant consent covering model training and downstream distribution. Scenario confidentiality is addressed in the master agreement.
Request a Hindi quote
Send the spec. You get scope, timeline and price.