White Papers
Maximum Entropy Confidence Estimation for Speech Recognition
Category: Software and Web Development
Tags: applications
Overview For many Automatic Speech Recognition (ASR) applications, it is useful to predict the likelihood that the recognized string contains an error. This paper explores two modifications of a classic design. First, it replaces the standard maximum likelihood classifier with a maximum entropy classifier. The maximum entropy framework carries the dual advantages discriminative training and reasonable generalization. Second, it includes a number of alternative features. The ASR system is heavily pruned, and often produces recognition lattices with only a single path. These alternate features are meant to serve as a surrogate for the typical features that can be computed from a rich lattice.
- Publisher
- Microsoft
- File Format
- Date Published
- Dec 13, 2008
- Format
- White Papers
- Topics
- Voice Recognition
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