From Brief to Production-Ready Delivery
A rigorous, technical approach to capturing Indian conversational voice. We combine managed sourcing, multi-layered annotation, and zero-compromise QA to fuel enterprise-grade Speech AI.
Requirement Scoping
Script Design
Speaker Sourcing
Managed Capture
Multi-pass Annotation
Technical QA
Secure Delivery
Managed Data Collection
Unlike scraped or synthetic data, our collection is human-in-the-loop. We record in 15+ acoustic environments including vehicles, bustling markets, and quiet offices to ensure robustness against real-world background noise.
Speaker Recruitment
Our network includes 5,000+ verified native speakers across India. We use psychometric and linguistic screening to ensure diversity in age, gender, and regional L1 influence, preventing demographic bias in your models.
Recording Protocol
Recordings use 48kHz / 16-bit linear PCM standards. We capture spontaneous speech via scenario-based prompting, ensuring natural pauses, fillers, and code-mixing (Hinglish) that scripted data misses.
Transcript Annotation
Each audio file undergoes three passes: (1) Verbatim transcription, (2) Language ID tagging for code-mixed turns, and (3) Speaker diarization with 100ms precision. Emotional and intent tags are added as required.
QA Process
100% of data is human-verified by senior linguists. We maintain a strict Error Rate (WER/CER) threshold of <0.5% for all gold-standard datasets. Metadata is validated using automated schema check scripts.
Delivery Format
Data is delivered in enterprise-standard layouts (JSON, XML, or CSV). We provide pre-split Train/Dev/Test subsets and detailed documentation on speaker metadata and environment profiles.
Standard Schema
Our datasets follow a strict file structure and metadata schema, ensuring immediate compatibility with standard ML frameworks like PyTorch and HuggingFace.
root/ ├── audio/ │ ├── session_001_mic_01.wav │ ├── session_001_mic_02.wav │ └── ... ├── transcripts/ │ ├── session_001.json │ └── ... ├── metadata.csv └── stats.json
{ "session_id": "SNX_HI_042", "utterances": [ { "speaker": "SPK_1", "start": "0.420", "end": "3.150", "text": "kal office band rahega?", "lang": "hinglish" } ], "environment": "indoor_ambient" }
QA Performance Standards
We measure quality across four technical vectors before any data is greenlit for delivery.
99.5% Transcription
Human-audited verbatim accuracy for every speaker turn.
Zero Hallucination
Strict validation against audio ensures no 'ghost' text or synthetic insertions.
< 50ms Diarization
Speaker boundaries are precisely synced with audio onset and offset.
Schema Validation
Automated JSON/CSV structural checks for zero metadata drift.
Governance &
Ethics Protocol
Data integrity isn't just technical; it's legal. Sonexis operates with a "Consent-by-Design" framework ensuring long-term IP safety for enterprise clients.
Explicit Opt-in
Every contributor signs a per-dataset agreement specifically for machine learning usage.
PII Scrubbing
Automated redaction of names, numbers, and addresses from both audio and text layers.
Fair Compensation
Transparent payment pipelines ensuring all contributors are paid above-market rates for their time.
GDPR Compliant
Global data protection standards applied to every session recorded in our network.
Ready to Benchmark Our Data?
Request a technical specification sheet or a sample snippet for your specific language pair and domain.