2,000 speakers of Telugu 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 Telugu specifically: Vowel-length contrasts are phonemic and short/long confusion changes meaning outright
• Code-mixing handled explicitly rather than edited out — Hyderabad speech mixes Telugu, Urdu/Deccani, Hindi and English. A Telugu dataset for Hyderabad deployment must include Urdu-origin vocabulary.

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.

Split cohorts explicitly between Telangana and Andhra Pradesh and tag every speaker; models trained without the tag cannot be evaluated per region.

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.

• Vowel-length contrasts are phonemic and short/long confusion changes meaning outright
• Hyderabad speech mixes Telugu, Urdu/Deccani, Hindi and English. A Telugu dataset for Hyderabad deployment must include Urdu-origin vocabulary.
• Coastal Andhra read speech dominates. Telangana rural and Rayalaseema speech is thin, despite Hyderabad being the largest deployment market.

• 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
• Telangana forms normalised to Coastal Andhra standard
• Long/short vowel marking errors under time pressure

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
• Telugu carries 4 recognised varieties across Andhra Pradesh, Telangana, parts of Karnataka and Odisha, so the quota matrix is wider than the headline volume suggests
• Coastal Andhra read speech dominates. Telangana rural and Rayalaseema speech is thin, despite Hyderabad being the largest deployment market.

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