Fine-Tune ASR for Your Domain with NVIDIA Speech Agent Skills

NVIDIA describes a workflow for adapting automatic speech recognition models to specialized domains using its Nemotron ASR orchestration skill and NeMo Speech fine-tuning skill, illustrated through air traffic control recordings that include radio noise, callsigns, and specialized phraseology. In experiments with Nemotron 3.5 ASR, staged adaptation using broad machine-labeled ATCO2 Silver data followed by refinement on a small human-labeled Gold set, while replaying general-English data, reduced ATC word error rate from 77.00% to 22.46% while keeping LibriSpeech general-English WER close to baseline at 3.63% versus 3.50%. A similarly structured Parakeet model reached 20.00% ATCO2 WER, while separate Jacktol experiments achieved 5.93% WER for Parakeet and 7.31% for Nemotron on that dataset’s test recordings. The article emphasizes preserving compatible model preprocessing, separating training, development, and test data, recording configurations and predictions, and evaluating domain performance alongside general-language retention. It also reports that n-gram aviation language models produced modest additional decoding improvements without changing acoustic model weights, and notes potential future work on synthetic aviation text, targeted word boosting, and error analysis.

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