Purpose The current intelligibility tests performed on speakers with atypical speech production are limited by the ability of listeners to restore distorted sequences. This results in a measure that is overvalued when compared with the real articulatory performance. In this article, we present a new intelligibility test in order to neutralize the commonly encountered bias in traditional perception-based assessments. We present the construction of the acoustic-phonetic decoding task and its first test during a perceptual judgment test of intelligibility and during a result comparison with a global perceptual evaluation. Method We developed a very large pseudoword directory including about 90,000 forms that respect French phonotactic constraints. From this directory, we have created lists of pseudowords intended to be recorded for the constitution of the corpus. These lists are established due to an algorithm integrating predefined linguistic constraints and produced by 47 speakers (nine healthy and 38 patients). We then performed a perceptual judgment of intelligibility test with 20 listeners who transcribed these productions. Results At the end of the data processing stage, we obtained a Perceived Phonological Deviation (PPD) score for each speaker that reflects the average number of features altered per phoneme. We then compared the PPD score with a global intelligibility score derived from a global perceptual assessment of intelligibility and of the alteration severity. Conclusions The current findings confirm that a speech intelligibility test based on pseudowords in French achieves fine-grained PPD scores, which enables discrimination between patients and healthy speakers. Moreover, the PPD score is related to the global intelligibility score, especially in severity. Further studies are needed to better understand the discrimination power of this intelligibility test based on an acoustic-phonetic decoding task.
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http://dx.doi.org/10.1044/2020_JSLHR-19-00088 | DOI Listing |
J Med Internet Res
January 2025
Department of Industrial and Systems Engineering, The University of Florida, GAINESVILLE, FL, United States.
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View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
School of Control Science and Engineering, Tiangong University, Tianjin, 300387, China.
With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalography (EEG) signals to address challenges in healthcare and brain-computer interface fields. The applications and major achievements of Graph Convolution Network (GCN) techniques in EEG signal analysis are reviewed in this paper. Through an exhaustive search of the published literature, a module-by-module discussion is carried out for the first time to address the current research status of GCN.
View Article and Find Full Text PDFEJNMMI Phys
January 2025
Department of Nuclear Medicine, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China.
Single photon emission computed tomography (SPECT), a technique capable of capturing functional and molecular information, has been widely adopted in theranostics applications across various fields, including cardiology, neurology, and oncology. The spatial resolution of SPECT imaging is relatively poor, which poses a significant limitation, especially the visualization of small lesions. The main factors affecting the limited spatial resolution of SPECT include projection sampling techniques, hardware and software.
View Article and Find Full Text PDFAdv Biotechnol (Singap)
January 2025
Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
Enzymes are the cornerstone of biocatalysis, biosynthesis and synthetic biology. However, their applicability is often limited by low substrate selectivity. A prime example is the bifunctional linalool/nerolidol synthase (LNS) that can use both geranyl diphosphate (GPP) and farnesyl diphosphate (FPP) to produce linalool and nerolidol, respectively.
View Article and Find Full Text PDFAbdom Radiol (NY)
January 2025
Kerman University of Medical Sciences, Kerman, Islamic Republic of Iran.
Background And Aim: Prior investigations of the natural history of abdominal aortic aneurysms (AAAs) have been constrained by small sample sizes or uneven assessments of aggregated data. Natural language processing (NLP) can significantly enhance the investigation and treatment of patients with AAAs by swiftly and effectively collecting imaging data from health records. This meta-analysis aimed to evaluate the efficacy of NLP techniques in reliably identifying the existence or absence of AAAs and measuring the maximal abdominal aortic diameter in extensive datasets of radiology study reports.
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