Vaani Benchmark V1.0 is a multimodal Hindi ASR dataset from 104 districts featuring spontaneous speech recordings in real-world conditions and three independent transcriptions per segment for robust multi-reference evaluation.
Code-switching in end-to-end automatic speech recognition: A systematic literature review,
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MultiClin benchmark shows multiscript-aware evaluation is fairer than single-reference metrics for clinical ASR, and script unification during training yields the best performance.
Agentic ASR adds closed-loop semantic correction to ASR and introduces S²ER, an LLM judge for meaning-level errors, showing larger gains on semantic than token metrics across multilingual benchmarks.
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Towards Human-Like Interactive Speech Recognition With Agentic Correction and Semantic Evaluation
Agentic ASR adds closed-loop semantic correction to ASR and introduces S²ER, an LLM judge for meaning-level errors, showing larger gains on semantic than token metrics across multilingual benchmarks.