1,000 hours of Hinglish Spontaneous Speech

Speakers talk unscripted on prompted topics — describing a process, recounting an event, arguing a position — with a moderator who prompts but does not lead.

A 60-minute session covering six to eight topics, recorded continuously and segmented afterwards into utterances at natural pause boundaries.

Yield at this style: 60–70% of recorded time is accepted. Long silences, moderator speech and abandoned topics are cut in post, so recorded hours run well ahead of delivered hours. Budget for the gap.

• Topic banks graded by familiarity, so speakers across education and occupation bands all have something to say
• Open prompts only — anything answerable with yes or no produces thirty seconds of audio and a stalled session
• Culturally grounded topics per region, since a prompt that works in Mumbai can draw blank looks in Guwahati
• Topic rotation across the cohort so the corpus does not over-represent a handful of subjects
• Built against Hinglish specifically: Intra-sentential switching means English words carry Indian phonology, so English acoustic models mis-transcribe them
• Code-mixing handled explicitly rather than edited out — 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.

Twelve to sixteen weeks. This is a programme with its own governance rather than a project — weekly demographic reconciliation, a standing protocol review and a named counterpart on your side.

Ten to twelve batches, fortnightly, with a formal acceptance test per batch. Rejected batches are re-recorded rather than patched, so schedule contingency is built into the plan.

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

At 1,000 hours the quota matrix is enforced per cell, not in aggregate. Every dialect, age and gender combination carries its own target and is signed off individually before final acceptance, because an aggregate 50/50 split can hide a cell that was never filled at all.

Screening is for willingness to talk, not literacy, which opens the cohort to speakers a scripted protocol would exclude. Expect to over-recruit by 15% for speakers who freeze on the day.

• Intra-sentential switching means English words carry Indian phonology, so English acoustic models mis-transcribe them
• 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.

• Segment boundary review — bad boundaries produce truncated words that poison training more than they help
• Disfluency transcription consistency audited across transcribers, since conventions drift fast on this style
• Moderator speech confirmed absent from delivered segments
• Topic distribution checked per speaker so one dominant subject does not skew the language model
• 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

100% technical QA, 7% content QA stratified across every axis, plus a blind 1% re-transcription audit measuring inter-annotator agreement across the whole programme.

• Speakers who dry up after a minute, leaving sessions that look complete by duration but are mostly silence
• Drift into a reading register when a speaker becomes self-conscious about the microphone
• Moderator over-prompting, which turns a monologue corpus into an interview corpus
• Transcriber normalisation — quietly cleaning up disfluencies destroys the exact signal this style exists to capture

Cost at this band is driven by: Programme management overhead, which is real at this scale and should be quoted explicitly rather than hidden in the hourly rate; The tail of rare dialect and demographic quotas.

• Transcriber consistency across a 45-person team is the dominant quality risk and needs continuous calibration, not a one-time briefing
• Speaker pool exhaustion in smaller cities, where the genuinely available cohort is finite
• Specification drift over three months as your model team learns what it actually needs — build a change-control step in rather than pretending it will not happen
• Hinglish carries 4 recognised varieties across Delhi NCR, Mumbai, Bengaluru, so the quota matrix is wider than the headline volume suggests
• 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.

• WAV audio to your naming convention, with the per-file manifest
• Verbatim Devanagari + Latin transcripts with utterance-level timestamps
• Filled-pause and false-start markers
• Segment offsets into the parent session file
• Topic label per segment
• Speech-rate and pause-density statistics per speaker
• Per-speaker metadata: age band, gender, region, dialect, education band
• Consent records mapped to speaker IDs
• QA report with pass rates, rejection reasons and agreement statistics
• Speaker-disjoint train / dev / test splits on request

• Robust ASR for real user speech
• Language modelling on natural syntax
• Disfluency detection and removal
• Prosody and speech-rate modelling

Phonetic balance cannot be guaranteed — speakers say what they say. If you need specific phone contexts or a controlled lexicon, this style has to be paired with a scripted layer.