Patent ID: 8620655

Claim:
A speech processing method, comprising: receiving a speech input which comprises a sequence of feature vectors; determining a likelihood of a sequence of words arising from the sequence of feature vectors using an acoustic model and a language model, comprising: providing an acoustic model for performing speech recognition on an input signal which comprises a sequence of feature vectors, said model having a plurality of model parameters relating to the probability distribution of a word or part thereof being related to a feature vector, wherein said speech input is a mismatched speech input which is received from a speaker in an environment which is not matched to the speaker or environment under which the acoustic model was trained; and adapting the acoustic model to the mismatched speech input, the speech processing method further comprising determining a likelihood of a sequence of features occurring in a given language using a language model; and 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 acoustic model to the mismatched speaker input comprises: relating speech from the mismatched speaker input to the speech used to train the acoustic model using: a mismatch function f for primarily modeling differences between the environment of the speaker and the environment under which the acoustic model was trained; and a speaker transform F for primarily modeling differences between the speaker of the mismatched speaker input, such that: y=f ( F ( x,v ), u ) where y represents the speech from the mismatched speaker input, x is the speech used to train the acoustic model, u represents at least one parameter for modeling changes in the environment and v represents at least one parameter used for mapping differences between speakers; and jointly estimating u and v, wherein said joint estimation of u and v is performed using the expectation maximization algorithm and comprises optimizing u and v in a single maximization step of said algorithm, wherein said at least one parameter u comprises parameters n and h, where n is used to model additive noise and h is used to model convolutional noise, and wherein said mismatch function f is of the form: y s = â¢ q â¡ ( x s , n s , h s ) = â¢ F â¡ ( x s ) + h s + Cln â¡ ( 1 + â…‡ C - 1 â¡ ( n s - F â¡ ( x s ) - h s ) ) where C the discrete cosine transformation matrix and the subscript s denotes static part.