Abstract:Benchmarking is critical for the systematic evaluation and comparison of automatic speech recognition (ASR) systems. While several open-source datasets are available for Hindi ASR, existing benchmarks remain limited in geographic diversity, demographic representation, and transcription robustness. We introduce an inclusive, multimodal Hindi ASR benchmark collected from 104 districts across India. The dataset consists of spontaneous speech elicited using image prompts and recorded in real-world acoustic conditions across diverse demographic groups. Each audio segment is annotated with three independent transcriptions, enabling multi-reference evaluation that accounts for permissible orthographic and lexical variations. This design supports more robust, inclusive, and realistic ASR evaluation. We benchmark multiple open-source and proprietary ASR models and report their comparative performance on the benchmark dataset.
From: Sujith Pulikodan [view email]
[v1]
Fri, 19 Jun 2026 13:20:16 UTC (5,324 KB)
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