2,000 speakers of Gujarati 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 Gujarati specifically: Murmured (breathy-voiced) vowels are phonemic in Gujarati and are absent from most shared Indic acoustic models
• Code-mixing handled explicitly rather than edited out — Business and trade vocabulary is heavily English; Gujarati diaspora speech adds further English structure. Specify whether diaspora speakers are in or out of scope.

Sixteen to twenty-two weeks, run in waves so that recruitment in the harder catchments starts early rather than being left to the end.

Ten to twelve batches with rolling demographic reconciliation, since a quota gap discovered in week eighteen is very expensive to close.

Surat and Rajkot recruitment is essential for dialect coverage; Ahmedabad-only cohorts sound uniform.

At 2,000 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.

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.

• Murmured (breathy-voiced) vowels are phonemic in Gujarati and are absent from most shared Indic acoustic models
• Business and trade vocabulary is heavily English; Gujarati diaspora speech adds further English structure. Specify whether diaspora speakers are in or out of scope.
• Very little spontaneous Gujarati audio exists publicly; nearly all of it is Ahmedabad read speech.

• 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
• Breathy vowels have no consistent orthographic marking
• Kathiyawadi lexical items replaced with standard equivalents

100% technical QA, 7% content QA stratified across every axis, blind re-transcription audit, and per-cell demographic sign-off before final acceptance.

• 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: Long-tail demographic cells, which dominate both cost and schedule at this size.

• The final 15% of the cohort — the rarest demographic cells — routinely takes as long as the first half and should be scheduled first, not last
• Speaker pool exhaustion is a hard constraint in all but the largest languages
• Voice-based duplicate detection across 2,000 speakers is a genuine engineering task, not a spreadsheet check
• Gujarati carries 5 recognised varieties across Gujarat, Daman & Diu, Dadra & Nagar Haveli, so the quota matrix is wider than the headline volume suggests
• Very little spontaneous Gujarati audio exists publicly; nearly all of it is Ahmedabad read speech.

• WAV audio to your naming convention, with the per-file manifest
• Verbatim Gujarati 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.