Modeling Filled Pauses in Medical Dictations

Modeling Filled Pauses in Medical Dictations Serge)' V.. Pakhomov University of Minnesota 190 Klaeber Court 320-16 th Ave. S.E Minneapolis, MN 55455 pakh0002@tc.umn.edu Abstract Filled pauses are characteristic of spontaneous speech and can present considerable problems for speech recognition by being often recognized as short words. An um can be recognized as thumb or arm if the recognizer's language model does not adequately represent FP's. Recognition of quasi-spontaneous speech (medical dictation) is subject to this problem as well. Results from medical dictations by 21 family practice physicians show that using an FP model trained on the corpus populated with FP's produces overall better results than a model trained on a corpus that excluded FP's or a corpus that had random FP's. Introduction Filled pauses (FP's), false starts, repetitions, fragments, etc. are characteristic of spontaneous speech and can present considerable problems for speech recognition. FP's are often recognized as short words of similar phonetic quality. For example, an um can be recognized as thumb or arm if the recognizer's language model does not adequately represent FP's. Recognition of quasi-spontaneous speech (medical dictation) is subject to this problem as well. The FP problem becomes especially pertinent where the corpora used to build language models are compiled from text with no FP's. Shriberg (1996) has shown that representing FP's in a language model helps decrease the model' s perplexity. She finds that when a FP occurs at a major phrase or discourse boundary, the FP itself is the best predictor of the following lexical material; conversely, in a non-boundary context, FP's are predictable from the preceding words. Shriberg (1994) shows that the rate of disfluencies grows exponentially with the length of the sentence, and that FP's occur more often in the initial position (see also Swerts (1996)). This paper presents a method of using bigram probabilities for extracting FP distribution from a corpus of hand- transcribed dam. The resulting bigram model is used to populate another Iraining corpus that originally had no FP's. Results from medical dictations by 21 family practice physicians show that using an FP model trained on the corpus populated with FP's produces overall better results than a model trained on a corpus that excluded FP's or a corpus that had random FP's. Recognition accuracy improves proportionately to the frequency of FP's in the speech. 1. Filled Pauses FP's are not random events, but have a systematic distribution and well-defined functions in discourse. (Shriberg and Stolcke 1996, Shriberg 1994, Swerts 1996, Macalay and Osgood 1959, Cook 1970, Cook and Lalljee 1970, Christenfeld, et al. 1991) Cook and Lalljee (1970) make an interesting proposal that FP's may have something to do with the listener's perception of disfluent speech. They suggest that speech may be more 619 comprehensible when it contains filler material during hesitations by preserving continuity and that a FP may serve as a signal to draw the listeners attention to the next utterance in order for the listener not to lose the onset of the following utterance. Perhaps, from …

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