A speech recognition system using a plurality of acoustic models which share probability distributions

A speech processing method comprising: receiving a speech input comprising a sequence of observations; determining the likelihood of a sequence of observations corresponding to a word or part thereof using a first acoustic model set with a first dictionary; determining the likelihood of a sequence of observations corresponding to a word or part thereof using a second acoustic model set with a second dictionary; and outputting text determined from said first and second acoustic models; wherein each model uses a plurality of pre-calculated probability distributions to determine the said likelihood and wherein probability distributions are shared between the models. The probability distributions may be Gaussian probability density functions associated with an acoustic model which may be a Hidden Markov Model (HMM). The acoustic models may be a phoneme model and a grapheme model. The observations may be converted into an n-dimensional feature vector in an n dimensional space which is then further converted into a plurality of sub-vectors each within a reduced dimension subspace of said n-dimensional space and said shared probability distributions may have been pre-calculated for said sub-spaces.