Automatic assessment of spoken language proficiency of non-native children
This work addresses the need for efficient language assessment in educational settings, but it is incremental as it applies existing methods to a specific domain.
The paper tackles the problem of automatically grading non-native children's spoken language proficiency in English and German by using ASR transcriptions and a feedforward neural network for scoring, achieving results through in-domain acoustic model adaptation.
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.