10. An unsupervised training system for an N-gram language model, comprising: a processor configured to: read recognition results obtained as a result of speech recognition of speech data; acquire a reliability for each of the read recognition results; refer to each recognition result's acquired reliability to select a subset of one or more N-gram entries based upon their respective reliabilities; and train an N-gram language model for one of more entries of the subset of N-gram entries using all recognition results, wherein the processor is further configured to select each of the N-gram entries, whose sum of a first number of appearances in a first corpus as a set of all the recognition results and 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 exceeds a predetermined number of times.
13. The system of claim 10 , wherein a posterior probability of a text unit obtained upon speech recognition of the speech data is used as the reliability in the acquiring.