Description

This is an updated and corrected version of the data set used by Sejnowski and Rosenberg in their influential study of speech generation using a neural network [1]. The file "nettalk.data" contains a list of 20,008 English words, along with a phonetic transcription for each word. The task is to train a network to produce the proper phonemes, given a string of letters as input. This is an example of an input/output mapping task that exhibits strong global regularities, but also a large number of more specialized rules and exceptional cases. Please see original readme file for more information.

Related Papers

  • Thomas G. Dietterich and Ghulum Bakiri. Solving Multiclass Learning Problems via Error-Correcting Output Codes. CoRR, csAI/9501101. 1995. [link]
  • Dietrich Wettschereck and David W. Aha. Weighting Features. ICCBR. 1995. [link]
  • Kai Ming Ting and Boon Toh Low. Model Combination in the Multiple-Data-Batches Scenario. ECML. 1997. [link]
  • Kai Ming Ting and Boon Toh Low. Theory Combination: an alternative to Data Combination. University of Waikato. [link]
  • Rong Jin and Yan Liu and Luo Si and Jaime Carbonell and Alexander G. Hauptmann. A New Boosting Algorithm Using Input-Dependent Regularizer. School of Computer Science, Carnegie Mellon University. [link]
  • Steven Salzberg. On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach. Data Min. Knowl. Discov, 1. 1997. [link]
  • Sherrie L. W and Zijian Zheng. A BENCHMARK FOR CLASSIFIER LEARNING. Basser Department of Computer Science The University of Sydney. [link]
  • Steve Whittaker and Loren G. Terveen and Bonnie A. Nardi. Let's stop pushing the envelope and start addressing it: a reference task agenda for HCI. a Senior Research Scientist in the Human Computer Interaction Department of AT&T; LabsResearch. [link]
  • Kai Ming Ting and Ian H. Witten. Issues in Stacked Generalization. J. Artif. Intell. Res. (JAIR, 10. 1999. [link]
  • Rayid Ghani. KDD Project Report Using Error-Correcting Codes for Efficient Text Classification with a Large Number of Categories. Center for Automated Learning and Discovery, School of Computer Science, Carnegie Mellon University. [link]
  • Wl/odzisl/aw Duch and Jerzy J. Korczak. Optimization and global minimization methods suitable for neural networks. Department of Computer Methods, Nicholas Copernicus University. [link]
  • [link]

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