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- W2912981563 abstract "We present a neural network approach to the automated assessment of non-native spontaneous speech in a listen and speak task. An attention-based Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is used to learn the relations (scoring rubrics) between the spoken responses and their assigned scores. Each prompt (listening material) is encoded as a vector in a low-dimensional space and then employed as a condition of the inputs of the attention LSTM-RNN. The experimental results show that our approach performs as well as the strong baseline of a Support Vector Regressor (SVR) using content-related features, i.e., a correlation of r = 0.806 with holistic proficiency scores provided by humans, without doing any feature engineering. The prompt-encoded vector improves the discrimination between the high-scoring sample and low-scoring sample, and it is more effective in grading responses to unseen prompts, which have no corresponding responses in the training set." @default.
- W2912981563 created "2019-02-21" @default.
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- W2912981563 date "2018-12-01" @default.
- W2912981563 modified "2023-09-26" @default.
- W2912981563 title "A Prompt-Aware Neural Network Approach to Content-Based Scoring of Non-Native Spontaneous Speech" @default.
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- W2912981563 doi "https://doi.org/10.1109/slt.2018.8639697" @default.
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