19. A language model creation method executed by at least one processor, comprising: acquiring a first content-specific language model which represents an appearance probability that a specific word appears in a first content, the first content comprising a first word sequence, a second content-specific language model which represents an appearance probability that the specific word appears in a second content, the second content comprising a second word sequence, a first probability parameter representing a probability that a content represented by a target word sequence is the first content, and a second probability parameter representing a probability that the content represented by the target word sequence is the second content, the target word sequence being at least a part of a speech recognition hypothesis generated in a speech recognition process; and creating, by the at least one processor, a language model based on the first probability parameter, the second probability parameter, the first content-specific language model and the second content-specific language model, the created language model representing a combined appearance probability which is a probability that the specific word appears within at least a portion of the target word sequence.
19. A language model creation method executed by at least one processor, comprising: acquiring a first content-specific language model which represents an appearance probability that a specific word appears in a first content, the first content comprising a first word sequence, a second content-specific language model which represents an appearance probability that the specific word appears in a second content, the second content comprising a second word sequence, a first probability parameter representing a probability that a content represented by a target word sequence is the first content, and a second probability parameter representing a probability that the content represented by the target word sequence is the second content, the target word sequence being at least a part of a speech recognition hypothesis generated in a speech recognition process; and creating, by the at least one processor, a language model based on the first probability parameter, the second probability parameter, the first content-specific language model and the second content-specific language model, the created language model representing a combined appearance probability which is a probability that the specific word appears within at least a portion of the target word sequence. 20. The language model creation method according to claim 19 , comprising: creating the language model such that the combined appearance probability increases as a sum of (i) a product of a first coefficient and the probability represented by the first content-specific language model, and (ii) a product of a second coefficient and the probability represented by the second content-specific language model, becomes larger, wherein the first coefficient increases in value as the first probability parameter becomes larger, the second coefficient increases in value as the second probability parameter becomes larger.