Estimation of vocal tract area function from volumetric Magnetic Resonance Imaging

Skordilis, Zisis Iason; Toutios, Asterios; Töger, Johannes; Narayanan, Shrikanth

doi:10.1109/icassp.2017.7952291

Cited by 9 publications

(7 citation statements)

References 19 publications

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“…Finally, to determine the practical use of the off‐resonance correction on an end use case, we measure vocal tract distance, which is a desired metric that is often used in the speech RT‐MRI analysis to obtain constriction degree or vocal tract area function . The distance metric is defined as the physical distance between the upper and lower boundaries shown in Figure A.…”

Section: Methodsmentioning

confidence: 99%

Dynamic off‐resonance correction for spiral real‐time MRI of speech

Lim

Lingala

Narayanan

et al. 2018

Magnetic Resonance in Med

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Section: Methodsmentioning

confidence: 99%

Dynamic off‐resonance correction for spiral real‐time MRI of speech

Lim

Lingala

Narayanan

et al. 2018

Magnetic Resonance in Med

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“…From the mid‐sagittal plane, we obtained grid lines that were perpendicular to the airway centerline obtained from an airway boundary segmentation method and extracted angled slices along the grid lines through the 3D volume (61 slices with 2‐mm increments). From each of the angled slices, we estimated the airway area [cm 2 ] encompassed by articulator boundaries from a region growing method, applied in this case to the dynamic data. Region growing was performed for each of the angled slices at every time frame independently with seed points automatically chosen as the intersection of the airway centerline from the mid‐sagittal plane, and the angled slices.…”

Section: Methodsmentioning

confidence: 99%

“…Detailed and direct 3D information about airway shape and spatiotemporal dynamics is essential to understanding speech production control and to relating articulation to speech acoustics. In the past, however, shaping imaging for speech has only been available indirectly from mid‐sagittal 2D dynamic MRI after transformation to 3D or in static volume from 2D multi‐planar imaging or in 3D for non‐natural and/or sustained phonation …”

Section: Introductionmentioning

confidence: 99%

“…In the past, however, shaping imaging for speech has only been available indirectly from mid-sagittal 2D dynamic MRI after transformation to 3D or in static volume from 2D multi-planar imaging or in 3D for non-natural and/or sustained phonation. [18][19][20][21] Recently, several research groups have demonstrated dynamic 3D MRI of the vocal tract. [22][23][24] Burdumy et al 22 proposed an imaging method with 200 × 200 × 62 mm 3 spatial coverage using variable density and stack-of-stars radial sampling patterns and measured dynamic modification of articulators during singing and speech tasks.…”

Section: Introductionmentioning

confidence: 99%

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3D dynamic MRI of the vocal tract during natural speech

Lim

Zhu

Lingala

et al. 2018

Magnetic Resonance in Med

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Purpose To develop and evaluate a technique for 3D dynamic MRI of the full vocal tract at high temporal resolution during natural speech. Methods We demonstrate 2.4 × 2.4 × 5.8 mm3 spatial resolution, 61‐ms temporal resolution, and a 200 × 200 × 70 mm3 FOV. The proposed method uses 3D gradient‐echo imaging with a custom upper‐airway coil, a minimum‐phase slab excitation, stack‐of‐spirals readout, pseudo golden‐angle view order in kx‐ky, linear Cartesian order along kz, and spatiotemporal finite difference constrained reconstruction, with 13‐fold acceleration. This technique is evaluated using in vivo vocal tract airway data from 2 healthy subjects acquired at 1.5T scanner, 1 with synchronized audio, with 2 tasks during production of natural speech, and via comparison with interleaved multislice 2D dynamic MRI. Results This technique captured known dynamics of vocal tract articulators during natural speech tasks including tongue gestures during the production of consonants “s” and “l” and of consonant–vowel syllables, and was additionally consistent with 2D dynamic MRI. Coordination of lingual (tongue) movements for consonants is demonstrated via volume‐of‐interest analysis. Vocal tract area function dynamics revealed critical lingual constriction events along the length of the vocal tract for consonants and vowels. Conclusion We demonstrate feasibility of 3D dynamic MRI of the full vocal tract, with spatiotemporal resolution adequate to visualize lingual movements for consonants and vocal tact shaping during natural productions of consonant–vowel syllables, without requiring multiple repetitions.

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“…This way we can obtain a comprehensive picture of the vocal tract, but due to the extended acquisition time, this picture is frozen. Such data can be successfully used for modeling vocal tract geometry [6,7] and for volumetric studies [8,9], both of which can subsequently find their uses (articulatory models [10], area functions [11]) with an implementation of temporal modeling and control that have to stem from elsewhere other than the MRI sequences. There are attempts to incorporate the temporal influence -coarticulatory effectsinto such static data [12,13], but the evidence is that attaining and maintaining a given static position for a period of time can be an insurmountable challenge for the speaker, resulting in unrealistic images, especially for producing liquids [14] and imposing control over nasalization.…”

Section: Introductionmentioning

confidence: 99%

Towards a Method of Dynamic Vocal Tract Shapes Generation by Combining Static 3D and Dynamic 2D MRI Speech Data

Douros¹,

Tsukanova²,

Isaieva³

et al. 2019

Interspeech 2019

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We present an algorithm for augmenting the shape of the vocal tract using 3D static and 2D dynamic speech MRI data. While static 3D images have better resolution and provide spatial information, 2D dynamic images capture the transitions. The aim of this work is to combine strong points of these two types of data to obtain better image quality of 2D dynamic images and extend the 2D dynamic images to the 3D domain. To produce a 3D dynamic consonant-vowel (CV) sequence, our algorithm takes as input the 2D CV transition and the static 3D targets for C and V. To obtain the enhanced sequence of images, the first step is to find a transformation between the 2D images and the mid-sagittal slice of the acoustically corresponding 3D image stack, and then find a transformation between neighbouring sagittal slices in the 3D static image stack. Combination of these transformations allows producing the final set of images. In the present study we first examined the transformation from the 3D mid-sagittal frame to the 2D video in order to improve image quality and then we examined the extension of the 2D video to the 3rd dimension with the aim to enrich spatial information.

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Estimation of vocal tract area function from volumetric Magnetic Resonance Imaging

Cited by 9 publications

References 19 publications

Dynamic off‐resonance correction for spiral real‐time MRI of speech

Dynamic off‐resonance correction for spiral real‐time MRI of speech

3D dynamic MRI of the vocal tract during natural speech

Towards a Method of Dynamic Vocal Tract Shapes Generation by Combining Static 3D and Dynamic 2D MRI Speech Data

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