Abstract: Disclosed are systems, methods, and products for language learning that may extract text from various resources having text, using various natural-language processing features, which can be combined with custom-designed learning activities to offer a needs-based, adaptive learning methodology. The system may receive a resource, extract keywords pedagogically valuable to non-native language learning and academic exercises. Metadata describing various aspects of resources from which keywords are extracted may be associated with keywords. Metadata describing various aspects of keywords may also be associated with keywords. Extracted keywords may be stored into a keyword store along with any metadata associated with keywords.

Abstract: Systems, methods, and products for language learning that may extract text from various resources having text, using various natural-language processing features, which can be combined with custom-designed learning activities to offer a needs-based, adaptive learning methodology. The system may receive a resource, extract keywords pedagogically valuable to non-native language learning and academic exercises. Metadata describing various aspects of resources from which keywords are extracted may be associated with keywords. Metadata describing various aspects of keywords may also be associated with keywords. Extracted keywords may be stored into a keyword store along with any metadata associated with keywords.

Abstract: Disclosed are systems, methods, and products for language learning software that automatically extracts text from resources using various natural-language processing features, which can be combined with custom-designed learning activities to offer a needs-based, adaptive learning methodology. The system may receive a text, extract keywords pedagogically valuable to non-native language learning, assign a difficulty score to the text using various linguistic attributes of the text, generate a list of potential distractors for each keyword related to a resource to implement in learning activities. Distractors may be of various types, which are dynamically selected from a distractor store depending on a learning activity chosen to meet a learner's needs. Distractors may vary in difficulty, and may be dynamically selected based on a learner's overall proficiency or based on a learner's abilities in specific language skills.

Abstract: A systems, methods, and products for language learning software that automatically extracts text from corpora using various natural-language processing product features, which can be combined with custom-designed learning activities to offer a needs-based, adaptive learning methodology. The system may receive a text, extract keywords pedagogically valuable to non-native language learning, assign a difficulty score to the text using various linguistic attributes of the text, generate a list of potential distractors for each keyword in the text to implement in learning activities, and topically tag the text against a taxonomy based on the content. This output is then used in conjunction with a series of learning activity-types designed to meet learners' language skill needs and then used to create dynamic, adaptive activities for them.

Abstract: Disclosed are systems, methods, and products for language learning that may extract text from various resources having text, using various natural-language processing features, which can be combined with custom-designed learning activities to offer a needs-based, adaptive learning methodology. The system may receive a resource, extract keywords pedagogically valuable to non-native language learning and academic exercises. Metadata describing various aspects of resources from which keywords are extracted may be associated with keywords. Metadata describing various aspects of keywords may also be associated with keywords. Extracted keywords may be stored into a keyword store along with any metadata associated with keywords.

Abstract: Disclosed are systems, methods, and products for language learning that automatically extracts keywords from resources using various natural-language processing product features, which can be combined with custom-designed learning activities to offer a needs-based, adaptive learning methodology. The system may receive resources having text and then determine a text difficulty score that predicts how difficult the resource is for language learners based on any number of factors, including any number of semantic and syntactic features of the text. Training resources labeled with metadata may be used to train a statistical model for determining difficulty scores of newly received text. Resources may be grouped based on difficulty score, and groups of resources may correspond to language learners' proficiency levels.

Abstract: A speech synthesis system and method is described. A speech segment database references speech segments having various different speech representational structures. A speech segment selector selects from the speech segment database a sequence of speech segment candidates corresponding to a target text. A speech segment sequencer generates from the speech segment candidates sequenced speech segments corresponding to the target text. A speech segment synthesizer combines the selected sequenced speech segments to produce a synthesized speech signal output corresponding to the target text.

Abstract: A speech synthesis system and method is described. A speech segment database references speech segments having various different speech representational structures. A speech segment selector selects from the speech segment database a sequence of speech segment candidates corresponding to a target text. A speech segment sequencer generates from the speech segment candidates sequenced speech segments corresponding to the target text. A speech segment synthesizer combines the selected sequenced speech segments to produce a synthesized speech signal output corresponding to the target text.