Automatic Assessment of Spoken Language Proficiency of Non-native Children
Roberto Gretter, Marco Matassoni, Katharina Allgaier, Svetlana Tchistiakova, Daniele Falavigna
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. Automatic scores are computed for low level proficiency indicators - such as: lexical richness, syntax correctness, quality of pronunciation, discourse fluency, semantic relevance to the prompt, etc - defined by human experts in language proficiency. A set of experiments was carried out on a large set of data collected during proficiency evaluation campaigns involving thousands of students, manually scored by human experts. Obtained results are presented and discussed.
BibTeX
@inproceedings{icassp2019_automaticassessm,
title = {Automatic Assessment of Spoken Language Proficiency of Non-native Children},
author = {Roberto Gretter and Marco Matassoni and Katharina Allgaier and Svetlana Tchistiakova and Daniele Falavigna},
booktitle = {ICASSP 2019},
year = {2019}
}