Naturalistic Dialogue Management for Noisy Speech Recognition

作者：Passonneau Rebecca J^*; Epstein Susan L; Ligorio Tiziana

来源：IEEE Journal of Selected Topics in Signal Processing, 2012, 6(8): 928-942.

DOI：10.1109/JSTSP.2012.2229964

• Human computer interaction
• machine learning
• robustness
• speech
• system performance

With naturalistic dialogue management, a spoken dialogue system behaves as a human would under similar conditions. This paper reports on an experiment to develop naturalistic clarification strategies for noisy speech recognition in the context of spoken dialogue systems. We collected a wizard-of-Oz corpus in which human wizards with access to a rich set of clarification actions made clarification decisions online, based on human-readable versions of system data. The experiment compares an evaluation of calls to a baseline system in a library domain with calls to an enhanced version of the system. The new system has a clarification module based on the wizard data that is a decision tree constructed from three machine-learned models. It replicates the wizards%26apos; ability to ground partial understandings of noisy input and to build upon them. The enhanced system has a significantly higher rate of task completion, greater task success and improved efficiency.

• 出版日期2012-12