Abstract
► We develop an automatic running speech intelligibility assessment method. ► We derive new text-independent speaker features from a phonological representation. ► We validate the method on a new corpus of patients treated for head and neck cancer. ► We show that it is as reliable as a human listener and able to track a speaker's progress. It is generally acknowledged that an unbiased and objective assessment of the communication deficiency caused by a speech disorder calls for automatic speech processing tools. In this paper, a new automatic intelligibility assessment method is presented. The method can predict running speech intelligibility in a way that is robust against changes in the text and against differences in the accent of the speaker. It is evaluated on a Dutch corpus comprising longitudinal data of several speakers who have been treated for cancer of the head and the neck. The results show that the method is as accurate as a human listener in detecting trends in the intelligibility over time. By evaluating the intelligibility predictions made with different models trained on distinct texts and accented speech data, evidence for the robustness of the method against text and accent factors is offered.
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