ORCID Profile
0000-0002-3070-4952
Current Organisation
University of Wisconsin Madison
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Publisher: Wiley
Date: 15-10-2013
DOI: 10.1002/LARY.23655
Abstract: To use classification algorithms to classify swallows as safe, penetration, or aspiration based on measurements obtained from pharyngeal high‐resolution manometry (HRM) with impedance. Case series evaluating new method of data analysis. Multilayer perceptron, an artificial neural network (ANN), was evaluated for its ability to classify swallows as safe, penetration, or aspiration. Data were collected from 25 disordered subjects swallowing 5‐ or 10‐mL boluses. Following extraction of relevant parameters, a subset of the data was used to train the models, and the remaining swallows were then independently classified by the ANN. A classification accuracy of 89.4 ± 2.4% was achieved when including all parameters. Including only manometry‐related parameters yielded a classification accuracy of 85.0 ± 6.0%, whereas including only impedance‐related parameters yielded a classification accuracy of 76.0 ± 4.9%. Receiver operating characteristic analysis yielded areas under the curve of 0.8912 for safe, 0.8187 for aspiration, and 0.8014 for penetration. Classification models show high accuracy in classifying swallows from dysphagic patients as safe or unsafe. HRM‐impedance with ANN represents one method that could be used clinically to screen for patients at risk for penetration or aspiration. Laryngoscope, 2013
Publisher: Elsevier BV
Date: 07-2022
Publisher: American Speech Language Hearing Association
Date: 14-01-2021
DOI: 10.1044/2020_JSLHR-19-00190
Abstract: This study aims to investigate the effects of dysphonic voice on speech intelligibility in Cantonese-speaking adults. Speech recordings from three speakers with dysphonia secondary to phonotrauma and three speakers with healthy voices were presented to 30 healthy listeners (15 men and 15 women M age = 22.7 years) under six noise conditions (signal-to-noise ratio [SNR] −10, SNR −5, SNR 0, SNR +5, SNR +10) and quiet conditions. The speech recordings were composed of sentences with five different lengths: five syllables, eight syllables, 10 syllables, 12 syllables, and 15 syllables. The effects of speaker's voice quality, background noise condition, and sentence length on speech intelligibility were examined. Speech intelligibility scores were calculated based on the listener's correct judgment of the number of syllables heard as a percentage of the total syllables in each stimulus. Dysphonic voices, as compared to healthy voices, were significantly more affected by background noise. Speech presented with dysphonic voices was significantly less intelligible than speech presented with healthy voices under unfavorable SNR conditions (SNR −10, SNR −5, and SNR 0 conditions). However, there was no sufficient evidence to suggest effects of sentence length on intelligibility, regardless of the speaker's voice quality or the level of background noise. This study provides empirical data on the impacts of dysphonic voice on speech intelligibility in Cantonese speakers. The findings highlight the importance of educating the public about the impacts of voice quality and background noise on speech intelligibility and the potential of compensatory strategies that specifically address these barriers. 0.23641/asha.13335926
Location: United States of America
Location: United States of America
Location: China
No related grants have been discovered for Jack J. Jiang.