Conversational AI Companies · Hinglish
Hinglish 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 Hinglish specifically, the work is shaped by 4 dialect varieties and by how much English enters the speech.
- Buyer profile
- Conversational AI Companies
- Language
- Hinglish (hi-Latn-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 Hinglish requires
- Intra-sentential switching means English words carry Indian phonology, so English acoustic models mis-transcribe them
- Dialects: Delhi corporate Hinglish, Mumbai Bambaiya, Call-centre register, Youth/social media register
- Hinglish is the code-mixing case itself. Typical urban customer-support speech is 30-60% English tokens embedded in Hindi grammar, with switching several times per utterance.
- Almost no public corpus contains genuine intra-sentential Hindi-English switching with per-token language tags. This is the highest-value gap for anyone building Indian conversational AI.
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
Recruit by switching behaviour, not by language proficiency. Screening recordings are used to confirm speakers switch naturally rather than performing one language.
| Dimension | Typical split | Why it matters for Hinglish |
|---|---|---|
| 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 Hinglish forms that younger urban speakers have lost |
| Region | Delhi NCR / Mumbai / Bengaluru and others | Dialect spread across 4 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 Hinglish 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 Hinglish capacity available now?
Recruit by switching behaviour, not by language proficiency. Screening recordings are used to confirm speakers switch naturally rather than performing one language. 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 Hinglish data?
Perpetual and transferable, with participant consent covering model training and downstream distribution. Scenario confidentiality is addressed in the master agreement.
Request a Hinglish quote
Send the spec. You get scope, timeline and price.