1. An automatic speech recognition method comprising: at a computer having one or more processors and memory for storing one or more programs to be executed by the processors: obtaining a plurality of speech corpus categories through classifying and calculating raw speech corpus; obtaining a plurality of classified language models that respectively correspond to the plurality of speech corpus categories through a language model training applied on each speech corpus category; obtaining an interpolation language model through implementing a weighted interpolation on each classified language model and merging the interpolated plurality of classified language models; constructing a decoding resource in accordance with an acoustic model and the interpolation language model; and decoding input speech using the decoding resource, and outputting a character string with a highest probability as a recognition result of the input speech, wherein obtaining a plurality of speech corpus categories through classifying and calculating raw speech corpus further comprises: calculating an affiliation matrix between terms based on the raw corpus; extracting term characteristics from the raw corpus using a term frequency—inverse document frequency (TF-IDF) method; implementing a dimension reduction method on the extracted term characteristics based on the affiliation matrix; and inputting the term characteristics after the dimension reduction into a classifier for training, and outputting the plurality of speech corpus categories; wherein calculating an affiliation matrix between terms based on the raw corpus further comprises: calculating co-occurrence rates between each term and any other term using equation CO ij = f ij × f ij d ij × d ij × f i × f j , and constructing a co-occurrence matrix based on the co-occurrence rates, wherein f ij is a number of times that term i occurs prior to term j, d ij is an average distance between term i and term j, f i is a term frequency of term i, and f j is a term frequency of term j; and calculating affiliation rates between each term and any other term using equation A ij =sqrt(ΣOR(CO ik , CO jk )ΣORΣOR(CO ki , CO kj )) based on the co-occurrence matrix, wherein OR is a logic OR operator and CO ij is a co-occurrence rate between term i and term j, and constructing the affiliation matrix based on the affiliation rates.
3. The method according to claim 1 , wherein the classifier is a support vector machine (SVM) classifier.