Articulation theory predicts that a subject's absolute or masked threshold configuration will affect the slope of the speech recognition performance-intensity (P-I) function. This study was carried out to test that prediction. Performance-intensity functions for the Technisonic Studios W-22 recordings were obtained from 12 subjects with normal hearing. Four continuous thermal noise maskers, high-pass (HP) noise, white noise, ANSI noise, and talker-spectrum-matched (TSM) noise, were used to shape threshold. P-I function slopes for the averaged data ranged from about 1.6%/dB in HP noise to about 6.7%/dB in TSM noise. At low to moderate speech intensity levels, the positions and slopes of the P-I functions were accurately estimated by an articulation index-type model that included corrections for subject proficiency and for high- and low-frequency spread of masking. At higher intensity levels, performance was overestimated by the model.
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http://dx.doi.org/10.1044/jshr.3702.439 | DOI Listing |
Sci Rep
January 2025
Institute for System Dynamics, University of Stuttgart, Waldburgstr. 19, 70563, Stuttgart, Germany.
Including sensor information in medical interventions aims to support surgeons to decide on subsequent action steps by characterizing tissue intraoperatively. With bladder cancer, an important issue is tumor recurrence because of failure to remove the entire tumor. Impedance measurements can help to classify bladder tissue and give the surgeons an indication on how much tissue to remove.
View Article and Find Full Text PDFJ Voice
January 2025
Division of Phoniatrics and Pediatric Audiology at the Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany.
Objectives: This study investigates the use of sustained phonations recorded during high-speed videoendoscopy (HSV) for machine learning-based assessment of hoarseness severity (H). The performance of this approach is compared with conventional recordings obtained during voice therapy to evaluate key differences and limitations of HSV-derived acoustic recordings.
Methods: A database of 617 voice recordings with a duration of 250 ms was gathered during HSV examination (HS).
Int J Pharm
January 2025
Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ, 08854, USA; Center for Structured Organic Particulate Systems (C-SOPS), Cranbury, NJ, 08512, USA.
This study used Raman and near-infrared (NIR) spectroscopy to monitor small real-time changes in powder blends and tablets in low-dose pharmaceutical formulations. The research aims to enhance process analytical technology (PAT) in pharmaceutical manufacturing, ensuring high-quality and uniform products with applications to produce drugs with narrow therapeutic indices (NTI). The study utilizes Raman and NIR spatially resolved spectroscopy (SRS) techniques to monitor a moderate cohesive material's active pharmaceutical ingredient (API) concentrations during manufacturing.
View Article and Find Full Text PDFAnal Biochem
January 2025
Key Laboratory of Green and Precise Synthetic Chemistry and Applications, Ministry of Education, Anhui Provincial Key Laboratory of Synthetic Chemistry and Applications, College of Chemistry and Materials Science, Huaibei Normal University, Huaibei, Anhui 235000, PR China. Electronic address:
Luminol-loaded mesoporous carbon nanospheres (MCs@LU) were utilized to develop a highly sensitive electrochemiluminescence (ECL) sensor for the detection of L-cysteine (L-Cys). L-Cys acted as the coreactant of luminol, and the pore confinement effect of mesoporous carbons (MCs) resulted in a robust ECL signal. Upon optimization, a linear correlation between the ECL intensity and L-Cys concentration was observed over the range of 5.
View Article and Find Full Text PDFMed Phys
January 2025
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real-time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image-guided adaptive RT systems, the use of deep learning has been considered.
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