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.

Download full-text PDF

Source
http://dx.doi.org/10.1080/02699206.2020.1852602DOI Listing

Publication Analysis

Top Keywords

rating-based intelligibility
24
intelligibility measurement
16
transcription- rating-based
12
intelligibility measurements
12
intelligibility
11
agreement transcription-
8
transcription-based measurement
8
auditory-perceptual evaluation
8
rating-based
7
measurement
6

Similar Publications

Rapid advancement in information technology promotes the growth of new online learning communities in an e-learning environment that overloads information and data sharing. When a new learner asks a question, how a system recommends the answer is the problem of the learner's cold start. In this article, our contributions are: (i) We proposed a Trust-aware Deep Neural Recommendation (TDNR) framework that addresses learner cold-start issues in informal e-learning by modeling complex nonlinear relationships.

View Article and Find Full Text PDF

Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction.

Sensors (Basel)

October 2024

Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada.

The first line of treatment for urinary incontinence is pelvic floor muscle (PFM) training, aimed at reducing leakage episodes by strengthening these muscles. However, many women struggle with performing correct PFM contractions or have misconceptions about their contractions. To address this issue, we present a novel PFM contraction quality assessment system.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System.

Sensors (Basel)

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.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!