Speaker-Conditioned Hierarchical Modeling for Automated Speech Scoring

Abstract

Automatic Speech Scoring (ASS) is the computer-assisted evaluation of a candidate's speaking proficiency in a language. ASS systems face many challenges like open grammar, variable pronunciations, and unstructured or semi-structured content. Recent deep learning approaches have shown some promise in this domain. However, most of these approaches focus on extracting features from a single audio, making them suffer from the lack of speaker-specific context required to model such a complex task. We propose a novel deep learning technique for non-native ASS, called speaker-conditioned hierarchical modeling. In our technique, we take advantage of the fact that oral proficiency tests rate multiple responses for a candidate. We extract context vectors from these responses and feed them as additional speaker-specific context to our network to score a particular response. We compare our technique with strong baselines and find that such modeling improves the model's average performance by 6.92% (maximum = 12.86%, minimum = 4.51%). We further show both quantitative and qualitative insights into the importance of this additional context in solving the problem of ASS.

Published in CIKM 2021

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• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Captum: A unified and generic model interpretability library for PyTorch
• SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring
• My Teacher Thinks The World Is Flat! Interpreting Automatic Essay Scoring Mechanism
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Cited by in corpus (1)

• AES Systems Are Both Overstable And Oversensitive: Explaining Why And Proposing Defenses