1. A computer-implemented method of processing textual adaptation data for creating a statistical language model that provides prior probability estimates for word sequences, the method comprising, with a computer: receiving textual adaptation data comprising textual data which is suitable for creating the statistical language model and non-dictated textual data which is not suitable for creating the statistical language model; segmenting the textual adaptation data into a sequence of units; extracting a first set of features for each unit in the sequence; normalizing the sequence of units to form a normalized sequence of units; extracting a second set of features for each unit in the normalized sequence of units; processing the data using a processor operating as a classifier to filter out the non-dictated textual data from the textual adaptation data, thereby identifying at least the textual data suitable for creating the language model, the processing including using a classification model which uses a combination of the first and second sets of features; outputting the textual data suitable for creating the statistical language model; and generating the statistical language model from the suitable data, wherein the statistical language model provides prior probability estimates for word sequences to guide a hypothesis search for a likely intended word sequence.
1. A computer-implemented method of processing textual adaptation data for creating a statistical language model that provides prior probability estimates for word sequences, the method comprising, with a computer: receiving textual adaptation data comprising textual data which is suitable for creating the statistical language model and non-dictated textual data which is not suitable for creating the statistical language model; segmenting the textual adaptation data into a sequence of units; extracting a first set of features for each unit in the sequence; normalizing the sequence of units to form a normalized sequence of units; extracting a second set of features for each unit in the normalized sequence of units; processing the data using a processor operating as a classifier to filter out the non-dictated textual data from the textual adaptation data, thereby identifying at least the textual data suitable for creating the language model, the processing including using a classification model which uses a combination of the first and second sets of features; outputting the textual data suitable for creating the statistical language model; and generating the statistical language model from the suitable data, wherein the statistical language model provides prior probability estimates for word sequences to guide a hypothesis search for a likely intended word sequence. 2. The computer-implemented method of claim 1 wherein processing includes using a linear classifier based classification model wherein the first set of features is based at least in part on a number of tokens and a number of characters in the sequence of units, and wherein the second set of features is based at least in part on a percentage of tokens requiring normalization in the normalized sequence of units.