Patent Document ID: 9536518
Application ID: 14643316

Base Claim:
1. 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 processing device is further configured to select 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.

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Claim 2:
2. The system of claim 1 , wherein each of the weights respectively given to each of the first number of appearances, the second number of appearances, and the third number of appearances is estimated in advance by an EM algorithm using a language model estimated from each of subsets of the first corpus, the second corpus, and the third corpus.