Deep Learning for Dialogic Spoken Language Assessment and Learning

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There is a growing demand for automatic approaches for assessing and improving spoken language proficiency with over one billion learners of English world-wide. Currently all spoken language assessment systems assume monologic (single speaker) speech or very constrained dialogic speech. One of the next fundamental challenges in spoken language assessment and learning is automatically assessing candidates in very natural interactive scenarios. Dialogic assessment, where the candidate is assessed through a 2 or 3 person conversation, allows them to demonstrate their speaking ability through the most natural form of interaction. This is commonly used for assessment, including for high stakes exams such as IELTS, and in learning/teaching, and allows both linguistic and communicative skills to be measured and improved. Automating dialogic assessment presents a number of significant challenges including no clear methodology for approaching the assessment, limited annotated training data compared with standard speech recognition tasks, with a wide range of accents and speaking styles, and the need to identify speakers automatically in the data. This work aims to take the latest advances in deep learning and apply them to dialogic assessment and learning.