Mixing 636 hours of LLM-generated synthetic conversations with 67 hours of real data outperforms a model trained on 2700 hours of real Hungarian speech on the BEA-Dialogue benchmark.
Automatic speech recognition for under-resourced languages: A survey
2 Pith papers cite this work. Polarity classification is still indexing.
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Machine interpreting should shift from fidelity metrics to three design priorities—agency, grounding, and experience—drawn from interpreting studies to close the usability gap with human-mediated communication.
citing papers explorer
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Efficient ASR Training with Conversations that Never Happened
Mixing 636 hours of LLM-generated synthetic conversations with 67 hours of real data outperforms a model trained on 2700 hours of real Hungarian speech on the BEA-Dialogue benchmark.
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Bridging the Usability Gap: Lessons from Interpreting Studies for Machine Interpreting Design
Machine interpreting should shift from fidelity metrics to three design priorities—agency, grounding, and experience—drawn from interpreting studies to close the usability gap with human-mediated communication.