This paper proposes an improved Goodness of Pronunciation (GoP) that utilizes
Uncertainty Quantification (UQ) for automatic speech intelligibility assessment
for dysarthric speech. Current GoP methods rely heavily on neural
network-driven overconfident predictions, which is unsuitable for assessing
dysarthric speech due to its significant acoustic differences from healthy
speech. 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 a
result, prior-normalized maxlogit GoP achieves the best performance, with a
relative increase of 5.66%, 3.91%, and 23.65% compared to the baseline GoP for
English, Korean, and Tamil, respectively. Furthermore, phoneme analysis is
conducted to identify which phoneme scores significantly correlate with
intelligibility scores in each language.

本文介绍了一种改进的发音好坏指标（GoP），它利用不确定性量化（UQ）进行自动评估失语症患者的语音清晰度，并通过归一化音素预测和修改评分函数来提高性能。