Patent Document ID: 8595006
Application ID: 12732700

Base Claim:
1. A speech recognition method implemented in a computer, comprising: receiving a speech input in a first noise environment which comprises a sequence of observations; determining the likelihood of a sequence of words arising from the sequence of observations using an acoustic model, comprising: providing an acoustic model for performing speech recognition on a input signal which comprises a sequence of observations, wherein said model has been trained to recognise speech in a second noise environment, said model having a plurality of model parameters relating to the probability distribution of a word or part thereof being related to an observation; adapting the model trained in the second environment to that of the first environment; the speech recognition method further comprising determining the likelihood of a sequence of observations occurring in a given language using a language model; combining the likelihoods determined by the acoustic model and the language model and outputting a sequence of words identified from said speech input signal, wherein adapting the model trained in the second environment to that of the first environment comprises: adapting the model parameters of the model trained in the second noise environment to those of the first noise environment using transform parameters to produce a target distribution, wherein the transform parameters have a block diagonal form and are regression class dependent, each regression class comprising a plurality of probability distributions; mimicking the target distribution using a linear regression type distribution, said linear regression type distribution comprising mimicked transform parameters; and estimating the mimicked transformed parameters.

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Claim 2:
2. The speech recognition method of claim 1 , wherein adapting the model parameters of the model trained in the second noise environment to that of the first noise environment comprises using a VTS-JUD compensation of the type: 
 p ( y|m )= N ( y;B (r m ) (μ x (m) −b (r m ) ), diag( B (r m ) (Σ x (m) +Σ b (r m ) B (r m ) T )) wherein y is the feature vector for noisy speech, m is a component index indicating a probability distribution relating a word or part thereof to a feature vector, μ x (m) is the mean of the distribution m in the second noise environment and Σ x (m) the covariance matrix of the distribution m in the second noise environment, and 
 A (r m ) =B (r m ) −1 =Σ x (r m ) (Σ yx (r m ) ) −1 , b (r m ) =μ x (r m ) −A (r m ) μ y (r m ) 
 Σ b (r m ) =A (r m ) Σ y (r m ) A (r m ) T −Σ x (r m ) where μ x (r m ) , μ y (r m ) , Σ x (r m ) , Σ y (r m ) , Σ xy (r m ) and Σ yx (r m ) are mean, covariance and cross-covariances for the second noise environment and first noise environment respectively in each regression class and r m is the regression class of component index m.