Graduate Program for Neuroscience-Computational Neuroscience, Boston University, MA.
Department of Speech, Language, and Hearing Sciences, Boston University, MA.
J Speech Lang Hear Res. 2019 Jul 15;62(7):2065-2081. doi: 10.1044/2019_JSLHR-S-MSC18-18-0187.
Purpose We empirically assessed the results of computational optimization and prediction in communication interfaces that were designed to allow individuals with severe motor speech disorders to select phonemes and generate speech output. Method Interface layouts were either random or optimized, in which phoneme targets that were likely to be selected together were located in proximity. Target sizes were either static or predictive, such that likely targets were dynamically enlarged following each selection. Communication interfaces were evaluated by 36 users without motor impairments using an alternate access method. Each user was assigned to 1 of 4 interfaces varying in layout and whether prediction was implemented (random/static, random/predictive, optimized/static, optimized/predictive) and participated in 12 sessions over a 3-week period. Six participants with severe motor impairments used both the optimized/static and optimized/predictive interfaces in 1-2 sessions. Results In individuals without motor impairments, prediction provided significantly faster communication rates during training (Sessions 1-9), as users were learning the interface target locations and the novel access method. After training, optimization acted to significantly increase communication rates. The optimization likely became relevant only after training when participants knew the target locations and moved directly to the targets. Participants with motor impairments could use the interfaces with alternate access methods and generally rated the interface with prediction as preferred. Conclusions Optimization and prediction led to increases in communication rates in users without motor impairments. Predictive interfaces were preferred by users with motor impairments. Future research is needed to translate these results into clinical practice. Supplemental Material https://doi.org/10.23641/asha.8636948.
目的 我们通过实证评估了旨在允许严重运动言语障碍者选择音素并生成言语输出的通信接口的计算优化和预测结果。
方法 接口布局为随机或优化，其中可能一起选择的音素目标位于附近。目标大小为静态或预测性的，使得可能的目标在每次选择后动态放大。使用替代访问方法，36 名无运动障碍的用户对通信接口进行了评估。每位用户被分配到 4 个接口之一，这些接口在布局和是否实施预测方面有所不同（随机/静态、随机/预测、优化/静态、优化/预测），并在 3 周内进行了 12 次会话。6 名严重运动障碍的参与者在 1-2 次会话中使用了优化/静态和优化/预测接口。
结果 在无运动障碍的个体中，预测在训练期间（第 1-9 次会话）提供了显著更快的通信率，因为用户正在学习接口目标位置和新的访问方法。训练后，优化显著提高了通信率。只有在训练后，当参与者了解目标位置并直接移动到目标时，优化才会变得相关。运动障碍患者可以使用替代访问方法的接口，并且通常将具有预测的接口评为首选。
结论 优化和预测导致无运动障碍用户的通信率提高。具有运动障碍的用户更喜欢预测接口。需要进一步的研究将这些结果转化为临床实践。
