5. The method according to claim 1 , wherein said context based information is selected from phonetic, linguistic and prosodic contexts.
1. A speech recognition method executed by processing circuitry programmed to implement speech recognition, said method comprising: receiving a speech input from a speaker which comprises a sequence of observations; and determining, using the processing circuitry, a likelihood of a sequence of words arising from the sequence of observations using an acoustic model, said acoustic model having a plurality of model parameters describing probability distributions which relate a word or part thereof to an observation, said acoustic model having been trained using first training data and adapted using second training data to said speaker, determining, using the processing circuitry, a likelihood of a sequence of observations occurring in a given language using a language model; and combining, using the processing circuitry, the likelihoods determined by the acoustic model and the language model and outputting a sequence of words identified from said speech input signal, wherein said acoustic model is context based for said speaker, said context based information being contained in said model using a plurality of decision trees, the structure of said decision trees being based on second training data, the decision trees splitting at nodes and wherein the structure is determined from the splitting of the nodes of the trees that has been calculated using maximum a posteriori criteria implemented as: ( m ^ MAP , λ ^ MAP ) = arg ⁢ max m , λ ⁢ { log ⁢ ⁢ p ⁡ ( O ❘ m , λ ) + α · log ⁢ ⁢ p ⁡ ( O ′ ❘ m , λ ) } where O′ is the first training data, O is the second training data, m denotes a parameter tying structure, λ is a set of HMM parameters, {circumflex over (m)} MAP denotes the parameter tying structure under maximum a posteriori criteria, {circumflex over (λ)} MAP are the HMM parameters under maximum a posteriori criteria and α is a parameter to be set.