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This paper presents a personalized ASR system for a dysarthric speaker by fine-tuning the Whisper foundation model, achieving 9.7% word error rate using only 22.5 hours of adaptation data plus 8.8 hours of user corrections. The results demonstrate that personalized fine-tuning can make foundation ASR models substantially more effective for dysarthric speech, with a deployed mobile application enabling real-world data collection.