Automatic View-Specific Assessment of L2 Spoken English

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The growing demand for learning English as a second language has increased interest in automatic approaches for assessing and improving spoken language proficiency. With Stefano Bannò (FBK)

FBK virtual event

Via Sommarive, Povo

A significant challenge in this field is to provide interpretable scores and informative feedback to learners through individual viewpoints of learners’ proficiency, as opposed to holistic scores. Thus far, holistic scoring remains commonly applied in large-scale commercial tests. As a result, an issue with more detailed evaluation is that human graders are generally trained to provide holistic scores.

This talk investigates whether view-specific systems can be trained when only holistic scores are available. To enable this process, view-specific networks are defined where both their inputs and structure are adapted to focus on specific facets of proficiency. It is shown that it is possible to train such systems on holistic scores, such that they provide view-specific scores at evaluation time. View-specific networks are designed in this way for pronunciation, rhythm, text, use of parts of speech and grammatical accuracy.  The relationships between the predictions of each system are investigated on the spoken part of the Linguaskill proficiency test. It is shown that the view-specific predictions are complementary in nature and capture different information about proficiency.


Stefano Bannò is a 3rd year PhD student in Cognitive Science at University of Trento in a joint programme with Fondazione Bruno Kessler (FBK) and is working on a project on automatic assessment of spoken language proficiency. As a part of his PhD, he has worked at the ALTA (Automated Language Teaching and Assessment) Institute of the Department of Engineering at Cambridge University as a visiting student. Before starting the doctoral course, he obtained a master’s degree in Philology and a bachelor’s degree in Classics at University of Trento. During his master’s studies, he spent a research period at the Lautarchiv of Humboldt University in Berlin.
Besides his academic career, he has worked as a musician and a secondary school teacher. His research interests span from machine learning and natural language processing to phonetics and sociolinguistics.

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