Journal of Communication Disorders and Assistive Technology – Online First

ISSN: 2576-3997

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jcdatAll the manuscripts published by ‘Journal of Communication Disorders and Assistive Technology’ undergo rapid, quality, and quick review processing by eminent editorial and review teams maintaining high standards and ethics of publishing. The scholarly content published online will be freely available to every reader anywhere in the world to read, download, copy, reuse and distribute, provided that the original work is properly cited.


Article (s)


Volume 4, Issue 1 J Commun Disorder Assist Technol, 2022

The Correlation Between Lifetime Smoking and Ethnicity in People Who Smoke: A Study of Vocal Characteristics

Marewa Glover and Marie-France Duhamel

Smoking is associated with the deterioration of voice health. Detection of deterioration of voice characteristics can be an early indicator of smoking-related laryngeal and lower respiratory diseases. This study aimed to determine if voice characteristics could be used to differentiate demographics and years of smoking among a diverse group of adults who smoke. Audio recordings were collected from fifty-nine adults aged 19-81 who currently smoked tobacco. Audio samples and a range of vocal measurements were analysed by gender, age, ethnicity, and the number of years smoked. Age, a proxy for years smoked, was not a significant factor in determining the vocal parameters used in this study. Gender had some influence on voice quality, specifically in measurements of the fundamental frequency, formant 4 and jitter. There was a correlation between ethnicity and vocal parameters for shimmer. Higher shimmer and noise-to-harmonic ratio values were found in smartphone-recorded audio samples. Ethnicity appeared to be a stronger proxy for years smoked than age. Historically higher tobacco consumption among the Indigenous Māori people of New Zealand could be one explanation for this. Variation caused by the type of recording method may be important for informing the development of remote mHealth electronic diagnostic voice quality apps or devices.




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