250 hours 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.

Six to eight weeks. Beyond three cities the bottleneck moves from recruitment to transcription throughput, which is why the transcriber count rises faster than the engineer count.

Four to five batches on a fortnightly cadence, sequenced so the dialects you care about most land first.

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

At 250 hours quotas are enforced per dialect and reconciled fortnightly. Aggregate demographics are reported per batch so drift is visible while there is still time to correct it.

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, 15% content QA with stratified sampling across city, dialect and transcriber, so no single team's output goes unchecked.

• 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: Transcription depth — verbatim with disfluencies costs materially more than clean-read transcription; Number of distinct dialect quotas rather than raw hours.

• Transcription becomes the critical path and stays there for the rest of the volume bands
• Coordinator turnover mid-build causes quota drift that is only visible in the final demographic report unless it is audited weekly
• 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.