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.
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. 4. The method according to claim 1 , wherein the weighted interpolation process is implemented on each classified language model based on an obscure degree of the respective speech corpus category, wherein the obscure degree of the speech corpus category is in a positive correlation with a weighted value.