Dysphonia negatively affects a speaker's intelligibility, especially in noisy environments. Previously, our study showed this effect of dysphonia with the transcription-based intelligibility measurement. While this finding indicates the importance of intelligibility assessment for this population, implementing the transcription-based measurement may be difficult in clinical settings due to its resource-demanding nature. Using the same speakers, this study examined the agreement between transcription- and rating-based intelligibility measurements. Six sentences from the Consensus of Auditory-Perceptual Evaluation of Voice (CAPE-V) were recorded from 18 individuals with dysphonia (6 adult females, 6 adult males, and 6 children). Their dysphonia severity was determined through auditory-perceptual evaluation by two speech-language pathologists. Cafeteria noise was added to these recordings at SNR0 and paired with a sample from a healthy speaker in their age and/or gender group. Forty-five listeners rated intelligibility of the dysphonic samples on a 7-point rating scale. Spearman's rank correlation tests were conducted to examine the correlations between rating-based intelligibility measurement and the transcription-based measurement from our previous study, as well as the voice quality ratings and the rating-based intelligibility measurements. There was a strong positive correlation between the transcription- and rating-based measurements at all noise levels. The correlation between rating-based intelligibility measurement and breathiness rating was also strong. Our findings suggest that the rating-based intelligibility measurement could potentially be used as a substitute for the transcription-based analysis. Furthermore, the intelligibility deficit may be particularly problematic to patients who present with breathy dysphonia.
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http://dx.doi.org/10.1080/02699206.2020.1852602 | DOI Listing |
PeerJ Comput Sci
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Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan.
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Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada.
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View Article and Find Full Text PDFHum Brain Mapp
March 2024
Department of Brain and Cognitive Engineering, Korea University, South Korea.
Time-resolved decoding of speed and risk perception in car driving is important for understanding the perceptual processes related to driving safety. In this study, we used an fMRI-compatible trackball with naturalistic stimuli to record dynamic ratings of perceived risk and speed and investigated the degree to which different brain regions were able to decode these. We presented participants with first-person perspective videos of cars racing on the same course.
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December 2023
University of South Florida, Department of Otolaryngology, Head & Neck Surgery, Tampa, Florida.. Electronic address:
Objectives: There is currently a lack of objective treatment outcome measures for transgender individuals undergoing gender-affirming voice care. Recently, Bensoussan et al developed an AI model that is able to generate a voice femininity rating based on a short voice sample provided through a smartphone application. The purpose of this study was to examine the feasibility of using this model as a treatment outcome measure by comparing its performance to human listeners.
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October 2022
AI Center, Tunghai University, No. 1727, Section 4, Taiwan Blvd, Xitun District, Taichung 407224, Taiwan.
The emerging field of eXplainable AI (XAI) in the medical domain is considered to be of utmost importance. Meanwhile, incorporating explanations in the medical domain with respect to legal and ethical AI is necessary to understand detailed decisions, results, and current status of the patient's conditions. Successively, we will be presenting a detailed survey for the medical XAI with the model enhancements, evaluation methods, significant overview of case studies with open box architecture, medical open datasets, and future improvements.
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