1,000 speakers of Kannada Scripted Speech

Speakers read prompts from a phonetically balanced script shown one line at a time, with retakes on any flubbed or misread line.

A 45-minute booth session yields roughly 300–400 prompts. One file per utterance, cut at the prompt boundary, so alignment is exact before any forced-alignment pass runs.

Yield at this style: 85–90% of recorded time is accepted. Prompt-level retakes catch problems inside the session, so very little is discarded afterwards. This is the highest-yield style we run.

• Script built for triphone coverage rather than word coverage, so rare phone contexts appear often enough to train on
• Digit strings, dates, currency and person names blocked separately, because these are where deployed ASR actually fails
• Domain lexicon injected from your product vocabulary when you supply one
• Sentence-length distribution spread deliberately, since all-short prompts produce a model that cannot handle long utterances
• Built against Kannada specifically: North Karnataka speech has markedly different intonation and lexicon from Mysuru standard
• Code-mixing handled explicitly rather than edited out — Bengaluru is a migration city: Kannada speech there is mixed with English, Hindi, Tamil and Telugu. Native-only Kannada cohorts recruited in Bengaluru are hard to fill without screening for years of residence.

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.

Screen Bengaluru participants for native fluency and years of Karnataka residence; otherwise the cohort drifts towards second-language Kannada.

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.

Requires literate speakers comfortable reading aloud in the target script, which is the main constraint on cohort breadth and has to be actively counterweighted.

• North Karnataka speech has markedly different intonation and lexicon from Mysuru standard
• Bengaluru is a migration city: Kannada speech there is mixed with English, Hindi, Tamil and Telugu. Native-only Kannada cohorts recruited in Bengaluru are hard to fill without screening for years of residence.
• Mysuru/Bengaluru standard dominates. North Karnataka (Dharwad, Kalaburagi) and coastal Mangaluru speech are barely represented in any public corpus.

• Prompt-to-audio match verified by a native reviewer; a misread line is a rejected file, not an edited transcript
• Hyperarticulation flagged — a speaker over-enunciating because they are reading produces audio that does not match deployment
• Clipping and truncation checked at both utterance boundaries
• Per-speaker prompt coverage confirmed, so no speaker silently skips a block
• Northern lexical items replaced with standard equivalents
• Inconsistent transliteration of English technical terms

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

• Reading voice: flat prosody and unnatural stress that trains a model on speech nobody actually produces
• Prompt fatigue in the back half of long sessions, where accuracy drops and pace flattens
• Speakers who are not fluent readers, which quietly biases the cohort toward higher education bands
• Script leakage across speakers, producing a corpus that memorises sentences instead of covering sounds

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
• Kannada carries 5 recognised varieties across Karnataka, parts of Maharashtra, Tamil Nadu and Andhra Pradesh, so the quota matrix is wider than the headline volume suggests
• Mysuru/Bengaluru standard dominates. North Karnataka (Dharwad, Kalaburagi) and coastal Mangaluru speech are barely represented in any public corpus.

• WAV audio to your naming convention, with the per-file manifest
• Verbatim Kannada transcripts with utterance-level timestamps
• Prompt ID mapped to every utterance
• Verbatim deviation flags where the speaker departed from the script
• Per-utterance SNR and duration in the manifest
• 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

• ASR acoustic model baselines
• TTS voice building where a single speaker is recorded at depth
• Pronunciation lexicon and G2P validation
• Forced-alignment and phone-boundary work

Scripted audio contains no disfluencies, no false starts and no natural turn-taking. A model trained on it alone degrades sharply on real spontaneous input, so it is a baseline layer rather than a complete training set.