We present our dysarthric speech recognition system submitted to the Interspeech 2025 Speech Accessibility Project Challenge. This challenge is a competition aimed at improving the recognition accuracy of dysarthric speech. In this challenge, we submitted a speech recognition system with high accuracy for dysarthric speech and achieved first place. In dysarthric speech recognition, models based on self-supervised learning are commonly used. However, we hypothesized that fine-tuning pre-trained model with inherently high recognition accuracy would achieve the best performance. To this end, we developed a model that combines various techniques, including data preprocessing to expand training datasets, data augmentation to enhance generalization during fine-tuning, and decoding acceleration to optimize inference speed. As a result, our speech recognizer greatly improved accuracy, reducing WER to 8.11 from the baseline Whisper large v2's 17.82 provided by the organizers.