The design of automatic speech pronunciation assessment can be categorized
into closed and open response scenarios, each with strengths and limitations. A
system with the ability to function in both scenarios can cater to diverse
learning needs and provide a more precise and holistic assessment of
pronunciation skills. In this study, we propose a Multi-task Pronunciation
Assessment model called MultiPA. MultiPA provides an alternative to Kaldi-based
systems in that it has simpler format requirements and better compatibility
with other neural network models. Compared with previous open response systems,
MultiPA provides a wider range of evaluations, encompassing assessments at both
the sentence and word-level. Our experimental results show that MultiPA
achieves comparable performance when working in closed response scenarios and
maintains more robust performance when directly used for open responses.

本研究提出了一种名为 MultiPA 的多任务发音评估模型，它相较于基于 Kaldi 的系统有更简单的格式要求以及与其他神经网络模型更好的兼容性，其实验结果表明在闭合反馈场景下 MultiPA 能够达到可比较的性能，同时在直接用于开放式反馈时保持更加稳健的表现。