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We are developing a real-time lecture transcription system for hearing impaired students in university classrooms. The automatic speech recognition (ASR) system is adapted to individual lecture courses and lecturers, to enhance the recognition accuracy. The ASR results are selectively corrected by a human editor, through a dedicated interface, before presenting to the students. An efficient adaptation scheme of the ASR modules has been investigated in this work. The system was tested for a hearing-impaired student in a lecture course on civil engineering. Compared with the current manual note-taking scheme offered by two volunteers, the proposed system generated almost double amount of texts with one human editor.