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Hidden Markov Model Hybrids

  • Alex GravesEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 385)

Abstract

In this chapter LSTM is combined with hidden Markov models (HMMs) to form a hybrid sequence labelling system (Graves et al., 2005b). HMM-neural network hybrids have been extensively studied in the literature, usually with MLPs as the network component. The basic idea is to use the HMM to model the sequential structure of the data, and the neural networks to provide localised classifications. The HMM is able to automatically segment the input sequences during training, and it also provides a principled method for transforming network classifications into label sequences. Unlike the networks described in previous chapters, HMM-ANN hybrids can therefore be directly applied to ‘temporal classification’ tasks with unsegmented target labels, such as speech recognition.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of TorontoTorontoCanada

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