  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).
