Hindi Teleconsultation Benchmark for Semantic Intent Alignment and Latency

100 high-fidelity, simulated clinical teleconsultation interactions in Hindi are provided to benchmark hybrid ASR-LLM systems. The dataset is designed to test the resolution of translation gaps, semantic drift, and phonetic errors by mapping colloquial symptom descriptions to SNOMED-CT and ICD-11 ontologies. It was authored by Aryan Raj Thakur and last updated on June 9, 2026.

Use Cases

• Benchmarking semantic fidelity of ASR outputs based on mapping to standardized medical ontologies.
• Evaluating end-to-end processing latency in hybrid edge-cloud telemedicine architectures.
• Testing the resolution of phonetic substitution errors in non-standard Hindi symptom descriptions.

Strengths

• Contains 100 simulated clinical interactions, providing a focused test set.
• Specifically designed to benchmark performance against known issues like semantic drift and translation gaps.
• Includes empirical logs for latency and semantic fidelity scores.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Data is simulated, which may not fully reflect real-world clinical interactions.
• Row count for the empirical logs is unknown, which may limit suitability assessment.

Provenance

Source

Harvard Dataverse

Collection Method

Simulated clinical teleconsultation interactions.

Freshness

Last updated 2026-06-09 15:27:22; freshness should be verified.