AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Findings identify evidence localization and fine-grained type discrimination as distinct challenges and show that compact supervised encoders are strong baselines for this task.
Results show consistent gains from lightweight domain adaptation on both held-out synthetic data and real-world recordings, confirming that synthetic data generation combined with LoRA-based fine-tuning is an effective and computationally practical strategy for improving ASR accuracy in specialized technical domains where labeled speech is scarce.
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