Abstract: A system includes an electronic device and a computer-readable storage medium. The computer-readable storage medium includes one or more programming instructions that, when executed, are configured to cause the electronic device to receive a selection of a seat, obtain one or more seat measurements associated with the seat, obtain one or more upper body measurements associated with a user, determine one or more user characteristics associated with the user, determine one or more body measurements for the user based on at least a portion of the one or more upper body measurements and/or at least a portion of the one or more user characteristics, generate a visual representation of the user relative to the seat based on at least a portion of the seat measurements and at least a portion of the body measurements, and cause the visual representation to be displayed on a display device of the electronic device.

Abstract: Methods for automated generation of speech sample asset production scores for users of a distributed language learning system, automated accent recognition and quantification and improved speech recognition. Utilizing a trained supervised machine learning module which is trained utilizing a training set comprising a plurality of production speech sample asset recordings, associated production scores generated by system users performing perception exercises and user background information. The trained supervised machine learning module may be configured for automated accent recognition, by feeding a candidate production speech sample asset so as to automate the generation of a speech sample asset production score and user background information. As such, the user background information may be translated into an accent type categorization and the speech sample asset production score may be translated into an accent strength.

Abstract: Methods for automated generation of speech sample asset production scores for users of a distributed language learning system, automated accent recognition and quantification and improved speech recognition. Utilising a trained supervised machine learning module which is trained utilising a training set comprising a plurality of production speech sample asset recordings, associated production scores generated by system users performing perception exercises and user background information. The trained supervised machine learning module may be configured for automated accent recognition, by feeding a candidate production speech sample asset so as to automate the generation of a speech sample asset production score and user background information. As such, the user background information may be translated into an accent type categorisation and the speech sample asset production score may be translated into an accent strength.