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.