Detecting structural metadata with decision trees and transformation-based learning.pdfVIP

Detecting Structural Metadata with Decision Trees and Transformation-Based Learning Joungbum Kim? and Sarah E. Schwarm? and Mari Ostendorf? ?Dept. of Electrical Engineering ?Dept. of Computer Science University of Washington Seattle, WA 98195. USA {bummie,sarahs,mo}@ Abstract The regular occurrence of disfluencies is a distinguishing characteristic of spontaneous speech. Detecting and removing such disflu- encies can substantially improve the usefulness of spontaneous speech transcripts. This pa- per presents a system that detects various types of disfluencies and other structural information with cues obtained from lexical and prosodic information sources. Specifically, combina- tions of decision trees and language models are used to predict sentence ends and interruption points and, given these events, transformation- based learning is used to detect edit disfluen- cies and conversational fillers. Results are re- ported on human and automatic transcripts of conversational telephone speech. 1 Introduction Automatic speech-to-text (STT) transcripts of sponta- neous speech are often difficult to comprehend even with- out the challenges arising from word recognition errors introduced by imperfect STT systems (Jones et al., 2003). Such transcripts lack punctuation that indicates clausal or sentential boundaries, and they contain a number of dis- fluencies that would not normally occur in written lan- guage. Repeated words, hesitations such as “um” and “uh”, and corrections to a sentence in mid-stream are a normal part of conversational speech. These disflu- encies are handled easily by human listeners (Shriberg, 1994), but their existence makes transcripts of sponta- neous speech ill-suited for most natural language pro- cessing (NLP) systems developed for text, such as parsers or information extraction systems. Similarly, the lack of meaningful segmentation in automatically generated speech transcripts makes them problematic to use in NLP systems, most of which are de