16. A computer program product for building an n-gram language model for an automatic speech recognition, the computer program product comprising a non-transitory computer usable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a 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.
16. A computer program product for building an n-gram language model for an automatic speech recognition, the computer program product comprising a non-transitory computer usable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a 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. 17. The computer program product according to claim 16 , wherein building the n-gram language model by the smoothing algorithm comprising: counting the n-gram counts using the training text data and the additional text data; and discounting the n-gram counts by the smoothing algorithm.