8. A system for building an n-gram language model for an automatic speech recognition, comprising: a processor; and a memory configured to store a program, which, when executed on the processor, performs the method for building the n-gram language model for the automatic speech recognition, the method comprising: reading training text data and additional text data both for the n-gram language model from a storage, wherein the additional text data comprise plural sentences having at least one target keyword; building the n-gram language model by a smoothing algorithm having discount parameters for n-gram counts, wherein each discount parameter for each target keyword is tuned using development data which are different from the additional text data so that a predetermined balance between precision and recall is achieved; decreasing erroneous detection of one or more target keywords by tuning all of the discount parameters to be larger, or increasing n-gram counts of each of a plurality of words erroneously detected as one of the one or more target keywords by tuning each of the discount parameters to be smaller; and performing spoken term detection based on the n-gram language model built using the development data.
8. A system for building an n-gram language model for an automatic speech recognition, comprising: a processor; and a memory configured to store a program, which, when executed on the processor, performs the method for building the n-gram language model for the automatic speech recognition, the method comprising: reading training text data and additional text data both for the n-gram language model from a storage, wherein the additional text data comprise plural sentences having at least one target keyword; building the n-gram language model by a smoothing algorithm having discount parameters for n-gram counts, wherein each discount parameter for each target keyword is tuned using development data which are different from the additional text data so that a predetermined balance between precision and recall is achieved; decreasing erroneous detection of one or more target keywords by tuning all of the discount parameters to be larger, or increasing n-gram counts of each of a plurality of words erroneously detected as one of the one or more target keywords by tuning each of the discount parameters to be smaller; and performing spoken term detection based on the n-gram language model built using the development data. 10. The system according to claim 8 , wherein the discount parameter for the target keyword is tuned to be larger to decrease erroneous detection of the target keyword, which erroneous detection occurred in a spoken term detection done by using the development data.