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The paper addresses adaptation methods to language model and speaking rate (SR) of individual speakers which are two major problems in automatic transcription of spontaneous presentation speech. To cope with a large variation in expression and pronunciation of words depending on the speaker, firstly, we investigate the effect of statistical and context-dependent pronunciation modeling. Secondly, we present unsupervised methods of language model adaptation to a specific speaker and a topic by 1) selecting similar texts based on the word perplexity and TF-IDF measure and 2) making direct use of the initial recognition result for generating an enhanced model. We confirm that all proposed adaptation methods and their combinations reduce the perplexity and word error rate. We also present a decoding strategy adapted to the SR. In spontaneous speech, SR is generally fast and may vary a lot. We also observe different error tendencies for portions of presentations where speech is fast or slow. Therefore, we propose a SR-dependent decoding strategy that applies the most appropriate acoustic analysis, phone models, and decoding parameters according to the SR. Several methods are investigated and their selective application leads to improved accuracy. The combined effect of the two proposed adaptation methods is also confirmed in transcription of real academic presentation.