By Topic

Simulation analysis for interactive retrieval of spoken documents with key terms ranked by reinforcement learning

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Yi-cheng Pan ; Grad. Inst. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei ; Lin-shan Lee

Unlike written documents, spoken documents are difficult to display on the screen; it is also difficult for users to browse these documents during retrieval. It has been proposed recently to use interactive multi-modal dialogues to help the user navigate through a spoken document archive to retrieve the desired documents. This interaction is based on a topic hierarchy constructed by the key terms extracted from the retrieved spoken documents. In this paper, the efficiency of the user interaction in such a system is further improved by a key term ranking algorithm using reinforcement learning with simulated users. Extensive simulation analysis was performed, and significant improvements in retrieval efficiency were observed. These improvements show the relative robustness to speech recognition errors.

Published in:

Spoken Language Technology Workshop, 2006. IEEE

Date of Conference:

10-13 Dec. 2006