2 research outputs found
Automatic assessment of spoken language proficiency of non-native children
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 assessment of spoken language proficiency of non-native children
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