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We investigate the optimal LM treatment of abundant filled pauses (FP) in spontaneous monologues of a professional dictation task. Questions addressed here are (1) how to deal with FP in the LM history and (2) to which extent can the LM distinguish between positions with high and low FP likelihood. Our results differ partly from observations reported on dialogues. Discarding FP from all LM histories clearly improves the performance. Local perplexities, entropies and word rankings at positions following FP suggest that most FP indicate hesitations rather than restarts. Proper prediction of FP allows to distinguish FP from word positions by a doubled FP probability. Recognition experiments confirm the improvements found in our perplexity studies.

LM studies on filled pauses in spontaneous medical dictation