Cultural Competency in Voice Evaluation: Considerations of Normative Standards for Sociolinguistically Diverse Voices

Procter, Teresa; Joshi, Ashwini

doi:10.1016/j.jvoice.2020.09.025

Cited by 10 publications

(8 citation statements)

References 33 publications

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“…These challenges include gender and age distribution, the presence of different mother tongues, dialects, sociolects, or cognitive aspects such as individual speech-language and reading competence that may affect various acoustic parameters. 30 , 31 , 32 , 33 , 34 , 35 , 36 Studies on COVID-19 face additional challenges related to the fact that COVID-19 is a relatively new and not yet well understood disease with a wide range of symptoms and divergent symptom severity. 37 , 38 Studies need to consider the symptom heterogeneity of COVID-19 positive patients and the fact that many symptoms may also occur in other diseases such as bronchial asthma or flu.…”

Section: Introductionmentioning

confidence: 99%

The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection

et al. 2024

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Section: Introductionmentioning

confidence: 99%

The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection

et al. 2024

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“…The degree to which the phonological features of a first language are transferred to a second language varies by speaker and is perceived as differences in the "strength" of an accent. 18,19 Thus, the degree of accentedness was documented by two SLPs. We predicted that experts' ratings for dysphonic speakers with foreign language accents would be higher (more severe) compared to those with GAE accents due to listener bias.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Foreign Language Accent on Expert Listeners' Auditory‐Perceptual Evaluations of Dysphonia

Marks,

Dahl,

Stepp

2023

The Laryngoscope

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IntroductionAuditory‐perceptual evaluations of dysphonia, though essential for comprehensive voice evaluation, are subject to listener bias. Knowledge of an underlying voice disorder can influence auditory‐perceptual ratings. Accented speech results in increased listener effort and delays in word identification. Yet, little is known about the impact of foreign language accents on auditory‐perceptual ratings for dysphonic speakers. The purpose of this work was to determine the impact of a foreign language accent on experts' auditory‐perceptual ratings of dysphonic speakers.MethodsTwelve voice‐specializing SLPs who spoke with a General American English (GAE) accent rated vocal percepts of 28 speakers with a foreign language accent and 28 with a GAE accent, all of whom had been diagnosed with a voice disorder. Speaker groups were matched based on sex, age, and mean smoothed cepstral peak prominence. Four linear mixed‐effects models assessed the impact of a foreign language accent on expert auditory‐perceptual ratings of the overall severity of dysphonia, roughness, breathiness, and strain.ResultsThe twelve raters demonstrated good inter‐ and intra‐rater reliability (ICC[3, k] = .89; mean ICC = .89). The linear mixed‐effects models revealed no significant impact of foreign language accent on ratings of overall severity of dysphonia, roughness, breathiness, or strain.ConclusionDespite the possibility of increased listener effort and bias, foreign language accent incongruence had no effect on expert listeners' auditory‐perceptual evaluations for dysphonic speakers. Findings support the use of auditory‐perceptual evaluations for voice disorders across sociolinguistically diverse populations.Level of Evidence3 Laryngoscope, 2023

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“…Although research on the automatic detection of diseases based on speech is rapidly expanding, it faces a number of challenges in terms of algorithm generalisability and potential application in real-world scenarios. These challenges include gender and age distribution, the presence of different mother tongues, dialects, sociolects, or cognitive aspects such as individual speech-language and reading competence that may affect various acoustic parameters (Alves et al, 2015; Goyal et al, 2021; Nagumo et al, 2020; Procter & Joshi, 2020; Rojas et al, 2020; Sun, 2020; Taylor et al, 2020). Studies on COVID-19 face additional challenges related to the fact that COVID-19 is a relatively new and not yet well understood disease with a wide range of symptoms and divergent symptom severity (Hu et al, 2020; Tu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

The Acoustic Dissection of Cough: Diving into Machine Listening-based COVID-19 Analysis and Detection

Ren

Chang

Bartl-Pokorny

et al. 2022

Preprint

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Purpose: The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19's transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge on the acoustic characteristics of COVID-19 cough sounds is limited, but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds. Methods: With the theory of computational paralinguistics, we analyse the acoustic correlates of COVID-19 cough sounds based on the COMPARE feature set, i. e., a standardised set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions. Results: The experimental results demonstrate that a set of acoustic parameters of cough sounds, e. g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, are relevant for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our automatic COVID-19 detection model performs significantly above chance level, i. e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positive/negative: 210/1,201). Conclusions: Based on the acoustic correlates analysis on the COMPARE feature set and the feature analysis in the effective COVID-19 detection model, we find that the machine learning method to a certain extent relies on acoustic features showing higher effects in conventional group difference testing.

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Cultural Competency in Voice Evaluation: Considerations of Normative Standards for Sociolinguistically Diverse Voices

Cited by 10 publications

References 33 publications

The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection

The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection

The Impact of Foreign Language Accent on Expert Listeners' Auditory‐Perceptual Evaluations of Dysphonia

The Acoustic Dissection of Cough: Diving into Machine Listening-based COVID-19 Analysis and Detection

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