Even though speech-recognition accuracy has improved significantly over the past 10 years, these systems do not currently generate/model structural information (meta-data) such as sentence boundaries (e.g., periods) or the form of a disfluency (e.g., in .I want [to go] * {I mean} meet with Fred., .to go. is an edit, which is signaled by an interruption point indicated as *, as well as an edit term .I mean..). Automatic detection of these phenomena would simultaneously improve parsing accuracy and provide a mechanism for cleaning up transcriptions for the downstream text processing modules. Similarly, constraints imposed by text processing systems such as parsers can be used to assign certain types of meta-data for correct identification of disfluencies.

The goal of this workshop is to investigate the enrichment of speech recognition output using parsing constraints and the improvement of parsing accuracy due to speech recognition enrichment. We will investigate the following questions: (1) How does the incorporation of syntactic knowledge affect sentence boundary and disfluency detection accuracy? (2) How does the availability of more accurate sentence boundaries and disfluency annotation affect parsing accuracy? This workshop project is interdisciplinary bringing together researchers from the speech recognition and natural language processing communities. The undergraduates on this project will be exposed to research that spans these two important areas, and will gain experience on approaches to interfacing between technologies in these two areas.

Opening Day Presentation
Closing Day Presentation
Final Report
SParseval Tool