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This paper proposes an improved Goodness of Pronunciation (GoP) that utilizesUncertainty Quantification (UQ) for automatic speech intelligibility assessmentfor dysarthric speech. Current GoP methods rely heavily on neuralnetwork-driven overconfident predictions, which is unsuitable for assessingdysarthric speech due to its significant acoustic differences from healthyspeech. To alleviate the problem, UQ techniques were used on GoP by 1)normalizing the phoneme prediction (entropy, margin, maxlogit, logit-margin)and 2) modifying the scoring function (scaling, prior normalization). As aresult, prior-normalized maxlogit GoP achieves the best performance, with arelative increase of 5.66%, 3.91%, and 23.65% compared to the baseline GoP forEnglish, Korean, and Tamil, respectively. Furthermore, phoneme analysis isconducted to identify which phoneme scores significantly correlate withintelligibility scores in each language.

Speech Intelligibility Assessment of Dysarthric Speech by using Goodness of Pronunciation with Uncertainty Quantification