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arXiv · 1903.06409

Automatic assessment of spoken language proficiency of non-native children

Roberto Gretter·Katharina Allgaier·Svetlana Tchistiakova·Daniele Falavigna
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Abstract

This paper describes technology developed to automatically grade Italian students (ages 9-16) on their English and German spoken language proficiency. The students' spoken answers are first transcribed by an automatic speech recognition (ASR) system and then scored using a feedforward neural network (NN) that processes features extracted from the automatic transcriptions. In-domain acoustic models, employing deep neural networks (DNNs), are derived by adapting the parameters of an original out of domain DNN.

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Roberto Gretter, Katharina Allgaier, Svetlana Tchistiakova, Daniele Falavigna. 2019-03-15. Automatic assessment of spoken language proficiency of non-native children. https://arxiv.org/abs/1903.06409

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