A TAG-based noisy-channel model of speech repairs

A TAG-based noisy channel model of speech repairs Mark Johnson Brown University Providence, RI 02912 mj@cs.brown.edu Eugene Charniak Brown University Providence, RI 02912 ec@cs.brown.edu Abstract This paper describes a noisy channel model of speech repairs, which can identify and correct repairs in speech transcripts. A syntactic parser is used as the source model, and a novel type of TAG-based transducer is the channel model. The use of TAG is motivated by the intuition that the reparandum is a “rough copy” of the repair. The model is trained and tested on the Switchboard disfluency-annotated corpus. 1 Introduction Most spontaneous speech contains disfluencies such as partial words, filled pauses (e.g., “uh”, “um”, “huh”), explicit editing terms (e.g., “I mean”), parenthetical asides and repairs. Of these repairs pose particularly difficult problems for parsing and related NLP tasks. This paper presents an explicit generative model of speech repairs and shows how it can eliminate this kind of disfluency. While speech repairs have been studied by psycholinguists for some time, as far as we know this is the first time a probabilistic model of speech repairs based on a model of syntactic structure has been described in the literature. Probabilistic models have the advantage over other kinds of models that they can in principle be integrated with other probabilistic models to produce a combined model that uses all avail- able evidence to select the globally optimal anal- ysis. Shriberg and Stolcke (1998) studied the lo- cation and distribution of repairs in the Switch- board corpus, but did not propose an actual model of repairs. Heeman and Allen (1999) de- scribe a noisy channel model of speech repairs, but leave “extending the model to incorporate higher level syntactic . . . processing” to future work. The previous work most closely related to the current work is Charniak and Johnson (2001), who used a boosted decision stub classi- fier to classify words as edited or not on a word by word basis, but do not identify or assign a probability to a repair as a whole. There are two innovations in this paper. First, we demonstrate that using a syntactic parser-based language model Charniak (2001) instead of bi/trigram language models signifi- cantly improves the accuracy of repair detection and correction. Second, we show how Tree Ad- joining Grammars (TAGs) can be used to pro- vide a precise formal description and probabilis- tic model of the crossed dependencies occurring in speech repairs. The rest of this paper is structured as fol- lows. The next section describes the noisy chan- nel model of speech repairs and the section af- ter that explains how it can be applied to de- tect and repair speech repairs. Section 4 evalu- ates this model on the Penn 3 disfluency-tagged Switchboard corpus, and section 5 concludes and discusses future work. 2 A noisy channel model of repairs We follow Shriberg (1994) and most other work on speech repairs by dividing a repair into three parts: the reparandum (the material repaired), the interregnum that is …

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