Relative fundamental frequency (RFF) is a promising acoustic measure for evaluating voice disorders. Yet, the accuracy of the current RFF algorithm varies across a broad range of vocal signals. The authors investigated how fundamental frequency (f) estimation and sample characteristics impact the relationship between manual and semi-automated RFF estimates.
View Article and Find Full Text PDFPurpose 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.
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