## Linear Gaussian models for speech recognition (2004)

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Venue: | CAMBRIDGE UNIVERSITY |

Citations: | 15 - 0 self |

### BibTeX

@TECHREPORT{Rosti04lineargaussian,

author = {Antti-Veikko Ilmari Rosti},

title = {Linear Gaussian models for speech recognition},

institution = {CAMBRIDGE UNIVERSITY},

year = {2004}

}

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### Abstract

Currently the most popular acoustic model for speech recognition is the hidden Markov model (HMM). However, HMMs are based on a series of assumptions some of which are known to be poor. In particular, the assumption that successive speech frames are conditionally independent given the discrete state that generated them is not a good assumption for speech recognition. State space models may be used to address some shortcomings of this assumption. State space models are based on a continuous state vector evolving through time according to a state evo-