Challenge
A Series B voice AI startup building an ASR engine for South Asian markets faced a data quality crisis. Their training data was studio-recorded standard Hindi and English - missing regional dialects (Bhojpuri, Awadhi, Kongu Tamil, Telangana Telugu) and samples with ambient noise, code-switching, and overlapping speech. Word Error Rates across regional dialects were catastrophically high in production despite satisfactory benchmark scores. They needed field-realistic multilingual speech data collection at scale, high-IAA annotation, and real-world model testing.
Solution
Phase 1 Data Collection: Field-recorded and crowdsourced 50,000+ hours across 12 Indian languages from our contributor network. Recording conditions varied: indoor, outdoor, mobile phone, landline, noisy environments. Phase 2 Audio Annotation: Verbatim transcription with disfluency markers. Speaker diarisation for multi-speaker recordings. Phoneme labeling for acoustic model training. IAA maintained at 0.82 or higher (Cohen Kappa) throughout. Phase 3 Data Cleaning and Validation: SNR-based audio quality filtering. Deduplication. PII redaction. Gold-standard validation against expert-transcribed reference corpus. Phase 4 Real-World Model Testing via TuTrain: Client retrained ASR model deployed to real users from 8 dialect groups. Failure modes identified fed back into targeted data collection (active learning loop).
Results
Key outcomes: 34% reduction in Word Error Rate across regional dialects. Tamil and Telugu dialect accuracy improved from 62% to 89%. TuTrain testing identified 3 critical failure modes that benchmark tests never revealed. Active learning loop targeted those failure modes in next collection cycle. Client secured Series C funding with improved model performance cited as key factor by investors.