--- title: "Hindi Speech Transcription Dataset for ASR Evaluation" description: "Devanagari or Latin, spelling flexibility skews every score. We scored candidates against an expert gold standard at the sub-character level." url: "https://unidata.pro/cases/hindi-speech-transcription-dataset-for-asr-evaluation/" date_modified: "2026-06-01T17:38:34+03:00" language: "en-US" --- **The Task** ------------ The client provided a batch of relatively clean, everyday Hindi audio recordings. The objective was straightforward: produce accurate transcriptions to serve as training and evaluation data. However, Hindi presents a structural nuance. It can be written both in Devanagari script and in Latin transliteration. Orthographic variation, spelling flexibility, and phonetic ambiguity can significantly impact scoring if not standardized. The key requirement was not just transcription, but measurable quality control at scale. **The Solution** ---------------- ### **Linguistic Grounding and Gold Standard Creation** Before publishing the vacancy, we needed a reliable benchmark. We engaged a Hindi language expert to complete a full transcription of selected audio samples. This annotated set became our gold standard and the foundation for evaluation logic. From this, we built the official test assignment for candidates. ### **Recruitment and Funnel Efficiency** We published the vacancy on LinkedIn. Results: - 165 applications in three days - Around 50% conversion into completed screening forms within one day - Candidates received access to the transcription tool the next day - Approximately 30 candidates completed the test task within two additional days This allowed us to move from sourcing to evaluation in less than a week. ### **Automated Quality Scoring with SERP** To eliminate subjective bias and scale evaluation, we implemented an automated scoring system based on SERP. SERP measures sub-character level mismatch between the gold transcription and the annotator’s output. Instead of broad accuracy estimation, it detects granular deviations between the reference text and the candidate submission. For each audio sample: - We calculated the error rate - Averaged the score across all samples - Established a threshold of 70% minimum quality Candidates scoring above 70% were approved and immediately invited to the project. This automation reduced manual review time and ensured consistent evaluation standards. ### **Production Launch and Ongoing Validation** Within one week: - The scoring logic was configured - Results were returned to candidates - 20 transcribers were onboarded The project is currently live and expected to run for approximately three months. A continuous validation layer is being finalized. The plan is to inject control samples with known match and mismatch patterns into the workflow. This will allow periodic quality checks without disrupting production and ensure sustained transcription accuracy over time. **Challenges and Specifics** ---------------------------- Unlike complex multimodal annotation projects, this case did not require tags, segmentation layers, or visual alignment. The workflow was intentionally minimalistic. The main specificity was linguistic. Hindi offers dual writing flexibility and rich phonetic structure. This makes transcription both accessible and subtly complex. Orthographic decisions influence scoring, and consistency becomes critical for dataset usability. Operationally, the project followed a classical annotation setup, but with strong automation and rapid deployment. ## Main Title Hindi Speech Transcription Dataset for ASR Evaluation ## Description Seven days from raw Hindi audio to a controlled, production-ready transcription system. Expert benchmark, automated SERP scoring, and a vetted team deployed without delay. For a client developing speech recognition technology, we designed a complete Hindi transcription workflow. From expert reference annotations to automated quality thresholds above 70 percent, every step was structured and measurable. The entire cycle from hiring to launch took one week. ## Hero **Project Duration:** 1.5 weeks ## Progress - Results - Quote ### Progress - Steps **List of Steps:** - **Number of days:** 5 days — **Step Description:** Pilot & Recruitment Campaign - **Number of days:** 2 days — **Step Description:** Test Completion - **Number of days:** 3 days — **Step Description:** Scoring System Configuration - **Number of days:** 1–2 days — **Step Description:** Results & Onboarding ### Results **List of Results:** - Rapid Deployment: Full recruitment, testing, scoring, and onboarding completed in one week - Structured Evaluation: Automated SERP-based scoring ensured objective candidate selection - Scalable Team: 20 qualified transcribers actively working - Quality Framework: Control task validation system in development ### Quote **Quote:** Hindi transcription seems simple until you measure it precisely. Script flexibility and phonetic nuance demand structured evaluation. When scoring logic is aligned with linguistic reality, quality becomes measurable and scalable. **Author:** Albina Romanova **Position:** Head of Speech Labeling & Data Generation [Full list of this site's AI-readable pages](https://unidata.pro/llms.txt)