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Learning Label Embeddings for Nearest-Neighbor Multi-class Classification with an Application to Speech Recognition

Author(s)
Singh-Miller, Natasha; Collins, Michael
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Abstract
We consider the problem of using nearest neighbor methods to provide a conditional probability estimate, P(y|a), when the number of labels y is large and the labels share some underlying structure. We propose a method for learning label embeddings (similar to error-correcting output codes (ECOCs)) to model the similarity between labels within a nearest neighbor framework. The learned ECOCs and nearest neighbor information are used to provide conditional probability estimates. We apply these estimates to the problem of acoustic modeling for speech recognition. We demonstrate significant improvements in terms of word error rate (WER) on a lecture recognition task over a state-of-the-art baseline GMM model.
Date issued
2009-12
URI
http://hdl.handle.net/1721.1/59333
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Journal
23rd Annual Conference on Neural Information Processing Systems, 2009
Publisher
Neural Information Processing Systems (NIPS) Foundation
Citation
Singh-Miller, Natasha and Michael Collins. "Learning Label Embeddings for Nearest-Neighbor Multi-class Classification with an Application to Speech Recognition."
Version: Author's final manuscript
ISBN
9781615679119

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