Speech data collection · हिन्दी
Hindi Speech Data Collection
Recruited-speaker speech corpora recorded to a written specification: scripted prompts, spontaneous monologue, or both, with full speaker metadata. This page covers how that works specifically for Hindi, where four-way stop contrast (voiced/voiceless x aspirated/unaspirated) that collapses in models trained on english-first acoustic units.
- Language
- Hindi (hi-IN)
- Dialects covered
- 7
- Typical programme
- 500-2,000 hours
- Cities
- Delhi, Lucknow, Jaipur
What changes when the language is Hindi
The service specification stays constant across languages; the linguistics do not. For Hindi, three things drive the design of a speech data collection programme.
- Four-way stop contrast (voiced/voiceless x aspirated/unaspirated) that collapses in models trained on English-first acoustic units
- Dialect spread: Khari Boli, Awadhi, Braj, Bhojpuri-influenced Hindi
- Urban Hindi speech is Hinglish in practice. Expect 15-40% English tokens in spontaneous speech: numbers, brands, technology terms, and whole clause switches. Any Hindi corpus that excludes English tokens will not match production traffic.
Technical specification
| Parameter | Standard |
|---|---|
| Sample rate | 48 kHz capture, delivered at 48/16 kHz as required |
| Bit depth | 24-bit capture, 16-bit PCM delivery |
| Format | WAV (PCM), one file per utterance or per session |
| Channels | Mono per speaker; multi-channel on request |
| Noise floor | Studio sessions below -50 dBFS; field sessions specified per project |
| Clipping | Zero tolerance; clipped takes are re-recorded, not repaired |
Hindi cohort design
Largest recruitment pool in the network. A 1,000-speaker Hindi cohort with balanced gender and 18-45 age bands is typically fielded across four cities to avoid a single-city accent bias.
| Dimension | Typical split | Why it matters for Hindi |
|---|---|---|
| Gender | 50 / 50 | Pitch range differences change acoustic model behaviour; unbalanced cohorts bias recognition |
| Age | 18-25: 30%, 26-40: 40%, 41-60: 30% | Older speakers retain conservative Hindi forms that younger urban speakers have lost |
| Region | Uttar Pradesh / Bihar / Madhya Pradesh and others | Dialect spread across 7 recognised varieties |
| Education | Mixed, including below-graduate | Prompt-reading fluency correlates with education and skews prosody |
| Condition | Studio / quiet room / field | Match the noise profile of your deployment |
Process
- Requirement lock: languages, hours, speaker count, demographic quotas, recording conditions
- Prompt design and linguistic review by native reviewers
- Speaker recruitment and screening against quota, with consent capture
- Recording sessions with real-time level and prompt-coverage monitoring
- Automated technical QA on every file (SNR, clipping, duration, silence)
- Native-speaker content QA on a defined sample, escalating to 100% on failure
- Packaging, manifest generation and delivery
Hindi-specific quality rules
- Inconsistent Devanagari vs romanised spelling for the same English loan word
- Nukta characters (क़ ख़ ग़ ज़ फ़) applied inconsistently by transcribers
- Numerals: whether to write digits, Devanagari numerals, or spelled-out words must be fixed in the style guide up front
- Honorific verb forms create long agreement chains that annotators shorten unless the guide forbids it
Every file passes automated technical checks. Content QA is sampled at 10% by default and raised per batch when the failure rate crosses the agreed threshold.
Deliverables
- Audio files in the agreed format and naming convention
- Per-utterance manifest (speaker ID, prompt ID, duration, condition)
- Speaker metadata: age band, gender, region, dialect, education band
- Consent records mapped to speaker IDs
- QA report with pass rates and rejection reasons
Worked example
A representative Hindi speech data collection engagement: 500 hours from 1,000 speakers, 50/50 gender, ages 18-45, spread across Delhi, Lucknow, Jaipur, recorded to the specification above and delivered in WAV with a per-utterance manifest.
Timeline: Typical: 3-6 weeks for 100-500 hours in a single language; multi-language programmes run in parallel.
Where this data is missing today
Public Hindi corpora skew heavily towards read newspaper text from educated urban speakers in Delhi and NCR. Rural Bihar and eastern UP speech, elderly speakers, and low-literacy speakers reading prompts aloud are largely absent.
Frequently asked
How much does Hindi speech data collection cost?
Priced per delivered hour or unit against a written spec. The cost drivers for Hindi are dialect spread, demographic narrowness and recording condition, in that order.
Which Hindi dialects are included?
By default Khari Boli, Awadhi, Braj, Bhojpuri-influenced Hindi and others, tagged per speaker. You can also commission a single-dialect corpus if you are targeting one region.
Can you deliver Hindi data in our format?
Yes. Audio files in the agreed format and naming convention is the default, but naming, schema and directory structure follow your pipeline.
How long does a Hindi programme take?
Typical: 3-6 weeks for 100-500 hours in a single language; multi-language programmes run in parallel.
Request a Hindi speech data collection quote
Hours, speakers, dialects, deadline. Send what you have.