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Automated Speech Scoring System Under The Lens: Evaluating and interpreting the linguistic cues for language proficiency ...
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Abstract:
English proficiency assessments have become a necessary metric for filtering and selecting prospective candidates for both academia and industry. With the rise in demand for such assessments, it has become increasingly necessary to have the automated human-interpretable results to prevent inconsistencies and ensure meaningful feedback to the second language learners. Feature-based classical approaches have been more interpretable in understanding what the scoring model learns. Therefore, in this work, we utilize classical machine learning models to formulate a speech scoring task as both a classification and a regression problem, followed by a thorough study to interpret and study the relation between the linguistic cues and the English proficiency level of the speaker. First, we extract linguist features under five categories (fluency, pronunciation, content, grammar and vocabulary, and acoustic) and train models to grade responses. In comparison, we find that the regression-based models perform equivalent ... : Accepted for publication in the International Journal of Artificial Intelligence in Education (IJAIED) ...
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Keyword:
Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD
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URL: https://arxiv.org/abs/2111.15156
https://dx.doi.org/10.48550/arxiv.2111.15156
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Multi-modal Automated Speech Scoring using Attention Fusion ...
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