Hinglish AI Training Data Collection

Hinglish is not a variant of a language your model already handles. The specific properties below are the ones that show up as errors in production.

• Intra-sentential switching means English words carry Indian phonology, so English acoustic models mis-transcribe them
• Switch points cluster around nouns, numbers, and discourse markers
• Indian English vowel realisations differ systematically from US/UK training data

Hinglish has 4 varieties that matter for data collection. Collecting only the standard variety produces a model that works in the capital and fails everywhere else.

• 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.

Specify your expected English-token ratio in the brief. It is cheaper to collect the right mix than to filter the wrong one afterwards.

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.

This is the practical reason to commission collection rather than assemble open corpora: the gap in the public data is exactly the part your users occupy.

These are the Hinglish-specific decisions that go into the style guide before any transcriber starts work.

• Whether English tokens are written in Latin or transliterated into Devanagari must be fixed by rule, not left to annotators
• Language-ID tagging per token is required for training but is skipped by most vendors
• Ambiguous words shared by both languages need an explicit tie-break rule

• Simulated customer-support calls with natural switching
• Two-party spontaneous conversation between colleagues
• Voice-assistant commands with English app and brand names
• Per-token language-tagged transcription

Recruit by switching behaviour, not by language proficiency. Screening recordings are used to confirm speakers switch naturally rather than performing one language.

Hinglish sessions run in Delhi, Gurugram, Noida, Mumbai, Bengaluru, Pune, Hyderabad. Multi-city fielding is the default because single-city cohorts carry an audible accent signature.

• 100% of delivered files pass automated technical QA for SNR, clipping, duration and silence
• 5-25% of files pass a second native-speaker content review, stratified by city, dialect and transcriber, and escalating to 100% on any batch that fails the agreed threshold
• Accepted yield runs 85-90% for scripted speech, 60-70% for spontaneous, 55-65% for conversational and 50-60% for telephony
• Default cohort quotas: 50/50 gender, with age bands at 30% (18-25), 40% (26-40) and 30% (41-60)
• 48 kHz / 24-bit capture, delivered as 16-bit PCM WAV, with studio sessions held below a -50 dBFS noise floor
• First response within one working day; a scoped, fixed quote within two to three

These are the figures a delivery is measured against, not aspirations. A batch that misses them is re-recorded at our cost rather than repaired.