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Speech intelligibility is crucial in language learning for effectivecommunication. Thus, to develop computer-assisted language learning systems,automatic speech intelligibility detection (SID) is necessary. Most of theworks have assessed the intelligibility in a supervised manner consideringmanual annotations, which requires cost and time; hence scalability is limited.To overcome these, this work proposes an unsupervised approach for SID. Theproposed approach considers alignment distance computed with dynamic-timewarping (DTW) between teacher and learner representation sequence as a measureto separate intelligible versus non-intelligible speech. We obtain the featuresequence using current state-of-the-art self-supervised representations fromWav2Vec-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).

Unsupervised speech intelligibility assessment with utterance level alignment distance between teacher and learner Wav2Vec-2.0 representations