The Role of Facial Action Units in Investigating Facial Movements During Speech

Investigating how facial movements can be used to characterize and quantify speech is important, in particular, to aid those suffering from motor control speech disorders. Here, we sought to investigate how facial action units (AUs), previously used to classify human expressions and emotion, could be used to quantify and understand unimpaired human speech. Fourteen (14) adult participants (30.1 ± 7.9 years old), fluent in English, with no speech impairments, were examined. Within each data collection session, 6 video trials per participant per phoneme were acquired (i.e., 102 trials total/phoneme). The participants were asked to vocalize the vowels /æ/, /ɛ/, /ɪ/, /ɒ/, and /ʊ/; the consonants /b/, /n/, /m/, /p/, /h/, /w/, and /d/; and the diphthongs /eI/, /ʌɪ/, /i/, /a:/, and /u:/. Through the use of Python Py-Feat, our analysis displayed the AU contributions for each phoneme. The important implication of our methodological findings is that AUs could be used to quantify speech in populations with no speech disability; this has the potential to be broadened toward providing feedback and characterization of speech changes and improvements in impaired populations. This would be of interest to persons with speech disorders, speech language pathologists, engineers, and physicians.

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Publication Details

Journal
Electronics
Published
2025-05-20
DOI
https://doi.org/10.3390/electronics14102066
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
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article

The Role of Facial Action Units in Investigating Facial Movements During Speech

Anthony Newby, Lara Thompson, N. F. Arnold, Ambika Bhatta et al.
Electronics
Emotion and Mood Recognition
article

The Role of Facial Action Units in Investigating Facial Movements During Speech

Anthony Newby, Lara Thompson, N. F. Arnold, Ambika Bhatta, Charles Kirkland
article en

Abstract

Investigating how facial movements can be used to characterize and quantify speech is important, in particular, to aid those suffering from motor control speech disorders. Here, we sought to investigate how facial action units (AUs), previously used to classify human expressions and emotion, could be used to quantify and understand unimpaired human speech. Fourteen (14) adult participants (30.1 ± 7.9 years old), fluent in English, with no speech impairments, were examined. Within each data collection session, 6 video trials per participant per phoneme were acquired (i.e., 102 trials total/phoneme). The participants were asked to vocalize the vowels /æ/, /ɛ/, /ɪ/, /ɒ/, and /ʊ/; the consonants /b/, /n/, /m/, /p/, /h/, /w/, and /d/; and the diphthongs /eI/, /ʌɪ/, /i/, /a:/, and /u:/. Through the use of Python Py-Feat, our analysis displayed the AU contributions for each phoneme. The important implication of our methodological findings is that AUs could be used to quantify speech in populations with no speech disability; this has the potential to be broadened toward providing feedback and characterization of speech changes and improvements in impaired populations. This would be of interest to persons with speech disorders, speech language pathologists, engineers, and physicians.

ElectronicsVol. 14(10)
University of the District of Columbia (US)
Openalex Percentile: Top 89%
Emotion and Mood Recognition
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