1,000 speakers of Marathi Conversational Speech

Two speakers hold an unscripted conversation seeded with a scenario, recorded on separate channels so overlap and turn-taking survive into the delivered files.

A 45–60 minute paired session with both speakers on isolated microphones, either in adjacent treated rooms or split-mic in one room with bleed measured and logged.

Yield at this style: 55–65% of recorded time is accepted. Overlap regions, crosstalk bleed and one-sided stretches all cost delivered time. The lowest-yield studio style we run, and priced accordingly.

• Scenario seeds rather than scripts — a disagreement to resolve, a plan to make, an experience to compare
• Pairing designed deliberately: familiar pairs produce natural interruption, stranger pairs produce polite turn-taking, and you need both
• Scenarios that invite disagreement, because agreeable conversation produces almost no overlap to train on
• Register mixed across pairs so the corpus is not uniformly formal
• Built against Marathi specifically: Retains the retroflex lateral ळ, which has no Hindi or English equivalent and is frequently substituted with ल by non-native transcribers
• Code-mixing handled explicitly rather than edited out — Mumbai and Pune speech mixes Marathi, Hindi, and English in the same sentence. Marathi-only recordings collected in Pune under-represent the Mumbai reality of tri-lingual switching.

Nine to twelve weeks, with recruitment running continuously rather than in a front-loaded phase.

Six to eight batches, speaker-disjoint by construction so held-out speakers are genuinely held out.

A representative Marathi cohort should be split roughly 40% western Maharashtra, 25% Vidarbha, 20% Marathwada, 15% Konkan rather than concentrated in Pune.

At 1,000 speakers 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.

Effectively doubles recruitment load, since speakers are booked in matched pairs and a single drop-out cancels the whole session. Plan on 20–25% over-recruitment.

• Retains the retroflex lateral ळ, which has no Hindi or English equivalent and is frequently substituted with ल by non-native transcribers
• Mumbai and Pune speech mixes Marathi, Hindi, and English in the same sentence. Marathi-only recordings collected in Pune under-represent the Mumbai reality of tri-lingual switching.
• Available Marathi speech data is dominated by standard Puneri read speech. Vidarbha, Marathwada, and coastal Konkan varieties are severely under-collected, which is exactly where deployed voice products lose accuracy.

• Channel bleed measured on every session and rejected above the agreed threshold
• Diarisation labels verified against the isolated channels rather than inferred from the mix
• Turn boundaries and overlap regions checked by a native listener
• Speaking-time balance per pair audited, so a dominant speaker does not silently halve the session's value
• ळ vs ल substitution by Hindi-trained transcribers
• Anusvara placement varies between conservative and modern orthography

100% technical QA, 10% content QA stratified by city and coordinator, with voice-based duplicate detection across the whole cohort.

• Crosstalk bleed that makes clean per-speaker training targets impossible to recover afterwards
• One speaker dominating, leaving a pair that delivers half the expected audio
• Unnatural politeness between strangers, producing clean but unrepresentative turn-taking
• Scheduling attrition — both speakers have to show up, so no-show rates compound rather than add

Cost at this band is driven by: Number of distinct demographic cells rather than total speakers.

• Speaker-disjoint splits are only trustworthy if duplicate detection is voice-based rather than ID-based, since one person can present two identities
• Coordinator-level demographic bias becomes measurable at this size and should be reported, not smoothed over
• Marathi carries 6 recognised varieties across Maharashtra, Goa, parts of Karnataka, so the quota matrix is wider than the headline volume suggests
• Available Marathi speech data is dominated by standard Puneri read speech. Vidarbha, Marathwada, and coastal Konkan varieties are severely under-collected, which is exactly where deployed voice products lose accuracy.

• WAV audio to your naming convention, with the per-file manifest
• Verbatim Devanagari transcripts with utterance-level timestamps
• Speaker-turn segmentation with start and end timestamps
• Overlap regions marked with participating speaker IDs
• Backchannel and interruption markers
• Per-pair relationship metadata: familiar or stranger
• 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

• Speaker diarisation
• Meeting and multi-party ASR
• Turn-taking and endpointing for voice agents
• Speaker separation and target-speaker extraction

Two-party conversation does not generalise to multi-party meetings with four or more speakers, where overlap statistics change substantially. Specify that case separately.