A rule-based language model for reading recognition

Systems for assessing and tutoring reading skills place unique requirements on underlying ASR technologies. Most responses to a “read out loud” task can be handled with a low perplexity language model, but the educational setting of the task calls for diagnostic measures beyond plain accuracy. Pearson developed an automatic assessment of oral reading fluency that was administered in the field to a large, diverse sample of American adults. Traditional N-gram methods for language modeling are not optimal for the special domain of reading tests because N-grams need too much data and do not produce as accurate recognition. An efficient rule-based language model implemented a set of linguistic rules learned from an archival body of transcriptions, using only the text of the new passage and no passage-specific training data. Results from operational data indicate that this rule-based language model can improve the accuracy of test results and produce useful diagnostic information.