Multilingual ASR Training Data for Voice AI Startup
How eQOURSE collected 50,000+ hours of multilingual speech data across 12 Indian languages and used real-world testing to reduce WER by 34% for a Series B voice AI startup.
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.