Communication-Oriented Automatic Assessment System for Accented Spoken Chinese in Read-Aloud Tasks
Huazhen Wang, Huan Wang, Jianguo Chen, Shiyue Zhu, Hao Zhou, Yifei Zhao
Abstract
The development of speech signal processing and deep learning has brought in many intelligent language learning tools. However, non-native Chinese learners (second-language or L2 learners) are often discouraged by language assessment applications on the market because of their accent. By contrast to artificial models, human experts usually give a higher score based on the performance of L2 learners in read-aloud tasks. In order to precisely assess the spoken Chinese of L2 learners in communication-oriented environments, we design a new assessment system, AsAsC (short for Assessment System for Accented Spoken Chinese) featuring comprehensive indexes and feasible quantitative schemes. Experiments on the real-world dataset show that AsAsC achieves a more human-like assessment capability in comparison to three typical enterprises’ assessment systems.
BibTeX
@inproceedings{icassp2024_communicationori,
title = {Communication-Oriented Automatic Assessment System for Accented Spoken Chinese in Read-Aloud Tasks},
author = {Huazhen Wang and Huan Wang and Jianguo Chen and Shiyue Zhu and Hao Zhou and Yifei Zhao},
booktitle = {ICASSP 2024},
year = {2024}
}