Using Active Shape Modeling Based on MRI to Study Morphologic and Pitch-Related Functional Changes Affecting Vocal Structures and the Airway

Miller, Nicola A.; Gregory, Jennifer S.; Aspden, Richard M.; Stollery, Pete; Gilbert, Fiona J.

doi:10.1016/j.jvoice.2013.12.002

Cited by 21 publications

(20 citation statements)

References 57 publications

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“…The feedback system consisted of a horizontal bar that continually updated to report on the participant's movement speed in the previous movement cycle. The participant was to keep the horizontal bar between the lower and higher boundaries (a 20% region, [72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88] BPM) of the 1.33-Hz target tempo (i.e., 80 BPM).…”

Section: Methodsmentioning

confidence: 99%

Gesture-speech physics in fluent speech and rhythmic upper limb movements

Pouw¹,

Jonge-Hoekstra²,

Harrison³

et al. 2020

Preprint

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Communicative hand gestures are often coordinated with prosodic aspects of speech, and salient moments of gestural movement (e.g., quick changes in speed) often co-occur with salient moments in speech (e.g., near peaks in fundamental frequency and intensity). A common understanding is that such gesture and speech coordination is culturally and cognitively acquired, rather than having a biological basis. Recently, however, the biomechanical physical coupling of arm movements to speech movements has been identified as a potentially important factor in understanding the emergence of gesture-speech coordination. Specifically, in the case of steady-state vocalization and mono-syllable utterances, forces produced during gesturing are transferred onto the tensioned body, leading to changes in respiratory-related activity and thereby affecting vocalization F0 and intensity. In the current experiment (N = 37), we extend this previous line of work to show that gesture-speech physics impacts fluent speech, too. Compared with non-movement, participants who are producing fluent self-formulated speech, while rhythmically moving their limbs, demonstrate heightened F0 and amplitude envelope, and such effects are more pronounced for higher-impulse arm versus lower-impulse wrist movement. We replicate that acoustic peaks arise especially during moments of peak-impulse (i.e., the beat) of the movement, namely around deceleration phases of the movement. Finally, higher deceleration rates of higher-mass arm movements were related to higher peaks in acoustics. These results confirm a role for physical-impulses of gesture affecting the speech system. We discuss the implications of gesture-speech physics for understanding of the emergence of communicative gesture, both ontogenetically and phylogenetically.

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Section: Methodsmentioning

confidence: 99%

Gesture-speech physics in fluent speech and rhythmic upper limb movements

Pouw¹,

Jonge-Hoekstra²,

Harrison³

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…The related literature includes studies on intensitybased segmentation [9][10][11][12] and statistical model-based segmentation 4,13 .…”

Section: Related Workmentioning

confidence: 99%

Segmentation of tongue shapes during vowel production in magnetic resonance images based on statistical modelling

Delmoral

Ventura

Tavares

2018

Proc Inst Mech Eng H

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Quantification of the anatomic and functional aspects of the tongue is pertinent to analyse the mechanisms involved in speech production. Speech requires dynamic and complex articulation of the vocal tract organs, and the tongue is one of the main articulators during speech production.Magnetic Resonance (MR) imaging has been widely used in speech related studies. Moreover, the segmentation of such images of speech organs is required to extract reliable statistical data.However, standards solutions to analyse a large set of articulatory images have not yet been established. Therefore, this article presents an approach to segment the tongue in 2D MR images and statistically model the segmented tongue shapes. The proposed approach assesses the articulator morphology based on an Active Shape Model, which captures the shape variability of the tongue during speech production. To validate this new approach, a dataset of mid-sagittal MR images acquired from four subjects was used, and key aspects of the shape of the tongue during the vocal production of relevant European Portuguese (EP) vowels were evaluated.

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“…On a different level, Miller et al (2014) gather vocal tract configuration data (also including the cervical spine and airway), from multiple speakers, by building an active shape model (ASM). Through the identification of independent modes of variation, and selecting those that account for the most part of the explained variance, the authors study coordinated changes affecting the cervical spine, airway and vocal structures.…”

Section: Related Workmentioning

confidence: 99%