Abstract: A computer-implemented system and method generate personalized text based on statistics derived from input received from a user representing the user's attempts to decode graphemes into phonemes. Such statistics may be measured and recorded at the grapheme-phoneme level, and may include substitutions, insertions, deletions, and correct utterances of phonemes by the user when reading text. A language model may be trained based on characteristics of the user, such as the user's age and/or reading grade level, and the personalized text may be generated after such training of the language model. Generating the personalized text may include generating a text creation prompt based on the statistics. The resulting text creation prompt may include a set of target words. The text creation prompt may be provided to the language model, which may generate the personalized text in response. The personalized text may include some or all of the target words.

Abstract: A computer-implemented system and method generate personalized text based on statistics derived from input received from a user representing the user's attempts to decode graphemes into phonemes. Such statistics may be measured and recorded at the grapheme-phoneme level, and may include substitutions, insertions, deletions, and correct utterances of phonemes by the user when reading text. A language model may be trained based on characteristics of the user, such as the user's age and/or reading grade level, and the personalized text may be generated after such training of the language model. Generating the personalized text may include generating a text creation prompt based on the statistics. The resulting text creation prompt may include a set of target words. The text creation prompt may be provided to the language model, which may generate the personalized text in response. The personalized text may include some or all of the target words.

Abstract: A computer implemented reading tutor comprises a player for outputting a response. An input block implementing a plurality of functions such as silence detection, speech recognition, etc. captures the read material. A tutoring function compares the output of the speech recognizer to the text which was supposed to have been read and generates a response, as needed, based on information in a knowledge base and an optional student model. The response is output to the user through the player. A quality control function evaluates the captured read material and stores the captured material in the knowledge base under certain conditions. An auto-enhancement function uses information available to the tutor to create additional resources such as identifying rhyming words, words with common roots, etc., which can be used as responses.