# Automatic assessment of spoken language proficiency of non-native children

## TL;DR

This paper presents an automated system for assessing non-native children's spoken language proficiency in English and German, utilizing speech recognition and neural network scoring based on adapted acoustic models.

## Contribution

It introduces a novel approach combining in-domain DNN acoustic models with neural network scoring for automatic language proficiency assessment.

## Key findings

- Effective automatic scoring of non-native children's speech
- Improved accuracy through adapted deep neural network acoustic models
- Demonstrated feasibility for language proficiency evaluation in educational settings

## 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.