Machine learning-based voice assessment for the detection of positive and recovered COVID-19 patients

This article provides a summary of some recent innovations in voice assessment expected to have an impact in the next 5–10 years on how patients with voice disorders are clinically managed by speech-language pathologists. Specific innovations discussed are in the areas of laryngeal imaging, ambulatory voice monitoring, and “big data” analysis using machine learning to produce new metrics for vocal health. Also discussed is the potential for using voice analysis to detect and monitor other health conditions.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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Cultural and Linguistic Adaptation of the Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V) Into Hindi

Journal of Speech Language and Hearing Research ◽

10.1044/2020_jslhr-20-00348 ◽

2020 ◽

Vol 63 (12) ◽

pp. 3974-3981

Author(s):

Ashwini Joshi ◽

Isha Baheti ◽

Vrushali Angadi

Keyword(s):

Strong Correlation ◽

Concurrent Validity ◽

Interrater Reliability ◽

Voice Quality ◽

Weak Correlation ◽

Voice Assessment ◽

Perceptual Evaluation ◽

Severity Grade ◽

Normal Voice ◽

Group A

Aim The purpose of this study was to develop and assess the reliability of a Hindi version of the Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V). Reliability was assessed by comparing Hindi CAPE-V ratings with English CAPE-V ratings and by the Grade, Roughness, Breathiness, Asthenia and Strain (GRBAS) scale. Method Hindi sentences were created to match the phonemic load of the corresponding English CAPE-V sentences. The Hindi sentences were adapted for linguistic content. The original English and adapted Hindi CAPE-V and GRBAS were completed for 33 bilingual individuals with normal voice quality. Additionally, the Hindi CAPE-V and GRBAS were completed for 13 Hindi speakers with disordered voice quality. The agreement of CAPE-V ratings was assessed between language versions, GRBAS ratings, and two rater pairs (three raters in total). Pearson product–moment correlation was completed for all comparisons. Results A strong correlation ( r > .8, p < .01) was found between the Hindi CAPE-V scores and the English CAPE-V scores for most variables in normal voice participants. A weak correlation was found for the variable of strain ( r < .2, p = .400) in the normative group. A strong correlation ( r > .6, p < .01) was found between the overall severity/grade, roughness, and breathiness scores in the GRBAS scale and the CAPE-V scale in normal and disordered voice samples. Significant interrater reliability ( r > .75) was present in overall severity and breathiness. Conclusions The Hindi version of the CAPE-V demonstrates good interrater reliability and concurrent validity with the English CAPE-V and the GRBAS. The Hindi CAPE-V can be used for the auditory-perceptual voice assessment of Hindi speakers.

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