Towards efficient human machine speech communication: The speech graffiti project

  • Authors:

  • Stefanie Tomko;Thomas K. Harris;Arthur Toth;James Sanders;Alexander Rudnicky;Roni Rosenfeld

  • Affiliations:

  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • ACM Transactions on Speech and Language Processing (TSLP)
  • Year:
  • 2005

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Abstract

This research investigates the design and performance of the Speech Graffiti interface for spoken interaction with simple machines. Speech Graffiti is a standardized interface designed to address issues inherent in the current state-of-the-art in spoken dialog systems such as high word-error rates and the difficulty of developing natural language systems. This article describes the general characteristics of Speech Graffiti, provides examples of its use, and describes other aspects of the system such as the development toolkit. We also present results from a user study comparing Speech Graffiti with a natural language dialog system. These results show that users rated Speech Graffiti significantly better in several assessment categories. Participants completed approximately the same number of tasks with both systems, and although Speech Graffiti users often took more turns to complete tasks than natural language interface users, they completed tasks in slightly less time.