1. A computer-implemented method for building an n-gram language model for an automatic speech recognition, comprising: reading training text data and additional text data for the n-gram language model from storage, wherein the additional text data comprises a plurality of 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.