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N-best Speech Hypotheses Reordering Using Linear Regression
Computer Science Department
  • Ananlada Chotimongkol, Carnegie Mellon University
  • Alexander I Rudnicky, Carnegie Mellon University
Date of Original Version
1-1-2001
Type
Conference Proceeding
Abstract or Description
We propose a hypothesis reordering technique to improve speech recognition accuracy in a dialog system. For such systems, additional information external to the decoding process itself is available, in particular features derived fromthe parse and the dialog. Such features can be combined with recognizer features by means of a linear regression model to predict the most likely entry in the hypothesis list. We introduce the use of concept error rate as an alternative accuracy measurement and compare it withy the use of word error rate. The proposed model performs better than human subjects performing the same hypothesis reordering task.
Citation Information
Ananlada Chotimongkol and Alexander I Rudnicky. "N-best Speech Hypotheses Reordering Using Linear Regression" (2001)
Available at: http://works.bepress.com/alexander_rudnicky/36/