Speech intelligibility is crucial in language learning for effective
communication. Thus, to develop computer-assisted language learning systems,
automatic speech intelligibility detection (SID) is necessary. Most of the
works have assessed the intelligibility in a supervised manner considering
manual annotations, which requires cost and time; hence scalability is limited.
To overcome these, this work proposes an unsupervised approach for SID. The
proposed approach considers alignment distance computed with dynamic-time
warping (DTW) between teacher and learner representation sequence as a measure
to separate intelligible versus non-intelligible speech. We obtain the feature
sequence using current state-of-the-art self-supervised representations from
Wav2Vec-2.0. We found the detection accuracies as 90.37\%, 92.57\% and 96.58\%,
respectively, with three alignment distance measures -- mean absolute error,
mean squared error and cosine distance (equal to one minus cosine similarity).

本研究提出一种基于无监督方法的自动语音可懂度检测技术，使用 Wav2Vec-2.0 的自监督表示，考虑使用使用动态时间规整算法（DTW）计算对齐距离来区分可懂语音和不可懂语音。通过验证，该方法的检测准确率分别为 90.37％、92.57％和 96.58％。