1. A non-transitory, computer readable storage medium having instructions stored thereon that, when executed by a computer, implement a training method for an N-gram language model, the method comprising: reading recognition results obtained as a result of speech recognition of speech data; acquiring a reliability for each of the read recognition results; referring to each recognition result's acquired reliability to select a subset of one or more N-gram entries based upon their respective reliabilities; and training the N-gram language model for one of more entries of the subset of N-gram entries using all recognition results, wherein each of the N-gram entries are selected by selecting from a first corpus, a second corpus, and a third corpus, each of the N-gram entries, whose sum of a first number of appearances in the first corpus as a set of all the recognition results, a second number of appearances in a second corpus as a subset of the recognition results with the reliability higher than or equal to a predetermined threshold value, and a third number of appearances in the third corpus as a baseline of the N-gram language model exceeds a predetermined number of times, where each of the first number of appearances, the second number of appearances, and the third number of appearances is given a different weight, respectively.
1. A non-transitory, computer readable storage medium having instructions stored thereon that, when executed by a computer, implement a training method for an N-gram language model, the method comprising: reading recognition results obtained as a result of speech recognition of speech data; acquiring a reliability for each of the read recognition results; referring to each recognition result's acquired reliability to select a subset of one or more N-gram entries based upon their respective reliabilities; and training the N-gram language model for one of more entries of the subset of N-gram entries using all recognition results, wherein each of the N-gram entries are selected by selecting from a first corpus, a second corpus, and a third corpus, each of the N-gram entries, whose sum of a first number of appearances in the first corpus as a set of all the recognition results, a second number of appearances in a second corpus as a subset of the recognition results with the reliability higher than or equal to a predetermined threshold value, and a third number of appearances in the third corpus as a baseline of the N-gram language model exceeds a predetermined number of times, where each of the first number of appearances, the second number of appearances, and the third number of appearances is given a different weight, respectively. 3. The computer readable storage medium of claim 1 , wherein the method further comprises training the selected one or more N-gram entries using all the recognition results and adding, to a base N-gram language model, the one or more N-gram entries and probabilities obtained as a result of the training.