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

arXiv: https://arxiv.org/abs/1903.06409 · HTML: https://mlsanity.com/paper/1903.06409/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Proficiency assessment of L2 spoken English using wav2vec 2.0](https://mlsanity.com/paper/2210.13168/) (2022)
- [Mixtures of Deep Neural Experts for Automated Speech Scoring](https://mlsanity.com/paper/2106.12475/) (2021)
- [LearnerVoice: A Dataset of Non-Native English Learners' Spontaneous Speech](https://mlsanity.com/paper/2407.04280/) (2024)
- [Semi-supervised Learning For Robust Speech Evaluation](https://mlsanity.com/paper/2409.14666/) (2024)

## Abstract (arXiv)

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.