Registration and statistical analysis of the tongue shape during speech production

This thesis analyzes the human tongue shape during speech production. First, a semi-supervised approach is derived for estimating the tongue shape from volumetric magnetic resonance imaging data of the human vocal tract. Results of this extraction are used to derive parametric tongue models. Next, a framework is presented for registering sparse motion capture data of the tongue by means of such a model. This method allows to generate full three-dimensional animations of the tongue. Finally, a multimodal and statistical text-to-speech system is developed that is able to synthesize audio and synchronized tongue motion from text.Diese Dissertation beschäftigt sich mit der Analyse der menschlichen Zungenform während der Sprachproduktion. Zunächst wird ein semi-überwachtes Verfahren vorgestellt, mit dessen Hilfe sich Zungenformen von volumetrischen Magnetresonanztomographie- Aufnahmen des menschlichen Vokaltrakts schätzen lassen. Die Ergebnisse dieses Extraktionsverfahrens werden genutzt, um ein parametrisches Zungenmodell zu konstruieren. Danach wird eine Methode hergeleitet, die ein solches Modell nutzt, um spärliche Bewegungsaufnahmen der Zunge zu registrieren. Dieser Ansatz erlaubt es, dreidimensionale Animationen der Zunge zu erstellen. Zuletzt wird ein multimodales und statistisches Text-to-Speech-System entwickelt, das in der Lage ist, Audio und die dazu synchrone Zungenbewegung zu synthetisieren.German Research Foundatio

Diphthong Synthesis using the Three-Dimensional Dynamic Digital Waveguide Mesh

The human voice is a complex and nuanced instrument, and despite many years of research, no system is yet capable of producing natural-sounding synthetic speech. This affects intelligibility for some groups of listeners, in applications such as automated announcements and screen readers. Furthermore, those who require a computer to speak - due to surgery or a degenerative disease - are limited to unnatural-sounding voices that lack expressive control and may not match the user's gender, age or accent. It is evident that natural, personalised and controllable synthetic speech systems are required. A three-dimensional digital waveguide model of the vocal tract, based on magnetic resonance imaging data, is proposed here in order to address these issues. The model uses a heterogeneous digital waveguide mesh method to represent the vocal tract airway and surrounding tissues, facilitating dynamic movement and hence speech output. The accuracy of the method is validated by comparison with audio recordings of natural speech, and perceptual tests are performed which confirm that the proposed model sounds significantly more natural than simpler digital waveguide mesh vocal tract models. Control of such a model is also considered, and a proof-of-concept study is presented using a deep neural network to control the parameters of a two-dimensional vocal tract model, resulting in intelligible speech output and paving the way for extension of the control system to the proposed three-dimensional vocal tract model. Future improvements to the system are also discussed in detail. This project considers both the naturalness and control issues associated with synthetic speech and therefore represents a significant step towards improved synthetic speech for use across society

Vocal tract sagittal slices estimation from MRI midsagittal slices during speech production of CV

International audienc

Observations on the dynamic control of an articulatory synthesizer using speech production data

This dissertation explores the automatic generation of gestural score based control structures for a three-dimensional articulatory speech synthesizer. The gestural scores are optimized in an articulatory resynthesis paradigm using a dynamic programming algorithm and a cost function which measures the deviation from a gold standard in the form of natural speech production data. This data had been recorded using electromagnetic articulography, from the same speaker to which the synthesizer\u27s vocal tract model had previously been adapted. Future work to create an English voice for the synthesizer and integrate it into a text-to-speech platform is outlined.Die vorliegende Dissertation untersucht die automatische Erzeugung von gesturalpartiturbasierten Steuerdaten für ein dreidimensionales artikulatorisches Sprachsynthesesystem. Die gesturalen Partituren werden in einem artikulatorischen Resynthese-Paradigma mittels dynamischer Programmierung optimiert, unter Zuhilfenahme einer Kostenfunktion, die den Abstand zu einem "Gold Standard" in Form natürlicher Sprachproduktionsdaten mißt. Diese Daten waren mit elektromagnetischer Artikulographie am selben Sprecher aufgenommen worden, an den zuvor das Vokaltraktmodell des Synthesesystems angepaßt worden war. Weiterführende Forschung, eine englische Stimme für das Synthesesystem zu erzeugen und sie in eine Text-to-Speech-Plattform einzubetten, wird umrissen

Numerical Modeling of Vocal Control and Patient-specific Surgical Planning of Type 1 Thyroplasty

This study aims to develop knowledge about the roles of intrinsic laryngeal muscles on voice control in both healthy and disordered conditions through comprehensive computational models. The phonation simulator was built by combining a three-dimensional high-fidelity MRI-based model of the larynx, active muscle mechanics, and fluid-structure-acoustic interaction model, which enabled the exploration of the underlayer mechanisms of the link between individual and/or group muscles contractions under both symmetric and asymmetric activations, vocal fold posture, vocal fold vibration, and voice outcomes during voice production. The first part of this research extensively investigated the effects of cricothyroid and thyroarytenoid muscle activations on voice characteristics through a parametric study. The role of these intrinsic muscles in the adjustment of geometrical and mechanical properties of vocal fold pre-phonatory posture, glottic flow aerodynamics, and acoustic and how all these components interact were explored. Results were comprehensively validated, and the link between elements of phonation was described in detail. In the next step, due to the model\u27s ability in the individual muscle activations, unilateral vocal fold paralysis was simulated, and the characteristics of disordered voice were analyzed. The voice simulator was then combined with the implant insertion model and genetic algorithm method to build a computational framework for patient-specific surgical planning of type 1 thyroplasty. This surgery is a standard procedure for treating unilateral vocal fold paralysis; however, it is subject to challenges mainly due to the small size of the implant and the high sensitivity of the voice outcome to the implant shape and position. Therefore, although the patient\u27s voice could be improved, the results might not be as satisfying as expected. Despite actual surgery, with very little room for try and error, the ideal implant could be achieved by optimizing the implant based on the patient\u27s desired voice using the presented computational framework. Both healthy and diseased cases and the corrected case using the optimized implant were simulated. Results revealed that the optimized implant could restore the aerodynamic and acoustic features of the disordered voice in producing a sustained vowel utterance. Furthermore, the performance of the implant in the pitch gliding test, which was simulated using temporal activation of the cricothyroid and thyroarytenoid muscles based on the first part of the study, was evaluated. In the final step, a physics-informed neural network-based algorithm was presented to reconstruct the three-dimensional cyclic vibration of vocal fold using two-dimensional sparse experimental data and laws of physics. Key acoustic parameters and vibratory dynamics of vocal folds and other parameters, such as flow rate, pressure distribution, and contact force, which are difficult to measure experimentally, were successfully predicted

A Silent-Speech Interface using Electro-Optical Stomatography

Sprachtechnologie ist eine große und wachsende Industrie, die das Leben von technologieinteressierten Nutzern auf zahlreichen Wegen bereichert. Viele potenzielle Nutzer werden jedoch ausgeschlossen: Nämlich alle Sprecher, die nur schwer oder sogar gar nicht Sprache produzieren können. Silent-Speech Interfaces bieten einen Weg, mit Maschinen durch ein bequemes sprachgesteuertes Interface zu kommunizieren ohne dafür akustische Sprache zu benötigen. Sie können außerdem prinzipiell eine Ersatzstimme stellen, indem sie die intendierten Äußerungen, die der Nutzer nur still artikuliert, künstlich synthetisieren. Diese Dissertation stellt ein neues Silent-Speech Interface vor, das auf einem neu entwickelten Messsystem namens Elektro-Optischer Stomatografie und einem neuartigen parametrischen Vokaltraktmodell basiert, das die Echtzeitsynthese von Sprache basierend auf den gemessenen Daten ermöglicht. Mit der Hardware wurden Studien zur Einzelworterkennung durchgeführt, die den Stand der Technik in der intra- und inter-individuellen Genauigkeit erreichten und übertrafen. Darüber hinaus wurde eine Studie abgeschlossen, in der die Hardware zur Steuerung des Vokaltraktmodells in einer direkten Artikulation-zu-Sprache-Synthese verwendet wurde. Während die Verständlichkeit der Synthese von Vokalen sehr hoch eingeschätzt wurde, ist die Verständlichkeit von Konsonanten und kontinuierlicher Sprache sehr schlecht. Vielversprechende Möglichkeiten zur Verbesserung des Systems werden im Ausblick diskutiert.:Statement of authorship iii Abstract v List of Figures vii List of Tables xi Acronyms xiii 1. Introduction 1 1.1. The concept of a Silent-Speech Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2. Structure of this work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2. Fundamentals of phonetics 7 2.1. Components of the human speech production system . . . . . . . . . . . . . . . . . . . 7 2.2. Vowel sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3. Consonantal sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4. Acoustic properties of speech sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5. Coarticulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.6. Phonotactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.7. Summary and implications for the design of a Silent-Speech Interface (SSI) . . . . . . . 21 3. Articulatory data acquisition techniques in Silent-Speech Interfaces 25 3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2. Scope of the literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3. Video Recordings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.4. Ultrasonography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.5. Electromyography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.6. Permanent-Magnetic Articulography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.7. Electromagnetic Articulography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.8. Radio waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.9. Palatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.10.Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4. Electro-Optical Stomatography 55 4.1. Contact sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.2. Optical distance sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.3. Lip sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.4. Sensor Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4.5. Control Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.6. Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5. Articulation-to-Text 99 5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.2. Command word recognition pilot study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.3. Command word recognition small-scale study . . . . . . . . . . . . . . . . . . . . . . . . 102 6. Articulation-to-Speech 109 6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.2. Articulatory synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.3. The six point vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 6.4. Objective evaluation of the vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . 116 6.5. Perceptual evaluation of the vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . 120 6.6. Direct synthesis using EOS to control the vocal tract model . . . . . . . . . . . . . . . . 125 6.7. Pitch and voicing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 7. Summary and outlook 145 7.1. Summary of the contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 7.2. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 A. Overview of the International Phonetic Alphabet 151 B. Mathematical proofs and derivations 153 B.1. Combinatoric calculations illustrating the reduction of possible syllables using phonotactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 B.2. Signal Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 B.3. Effect of the contact sensor area on the conductance . . . . . . . . . . . . . . . . . . . . 155 B.4. Calculation of the forward current for the OP280V diode . . . . . . . . . . . . . . . . . . 155 C. Schematics and layouts 157 C.1. Schematics of the control unit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 C.2. Layout of the control unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 C.3. Bill of materials of the control unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 C.4. Schematics of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 C.5. Layout of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 C.6. Bill of materials of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 D. Sensor unit assembly 169 E. Firmware flow and data protocol 177 F. Palate file format 181 G. Supplemental material regarding the vocal tract model 183 H. Articulation-to-Speech: Optimal hyperparameters 189 Bibliography 191Speech technology is a major and growing industry that enriches the lives of technologically-minded people in a number of ways. Many potential users are, however, excluded: Namely, all speakers who cannot easily or even at all produce speech. Silent-Speech Interfaces offer a way to communicate with a machine by a convenient speech recognition interface without the need for acoustic speech. They also can potentially provide a full replacement voice by synthesizing the intended utterances that are only silently articulated by the user. To that end, the speech movements need to be captured and mapped to either text or acoustic speech. This dissertation proposes a new Silent-Speech Interface based on a newly developed measurement technology called Electro-Optical Stomatography and a novel parametric vocal tract model to facilitate real-time speech synthesis based on the measured data. The hardware was used to conduct command word recognition studies reaching state-of-the-art intra- and inter-individual performance. Furthermore, a study on using the hardware to control the vocal tract model in a direct articulation-to-speech synthesis loop was also completed. While the intelligibility of synthesized vowels was high, the intelligibility of consonants and connected speech was quite poor. Promising ways to improve the system are discussed in the outlook.:Statement of authorship iii Abstract v List of Figures vii List of Tables xi Acronyms xiii 1. Introduction 1 1.1. The concept of a Silent-Speech Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2. Structure of this work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2. Fundamentals of phonetics 7 2.1. Components of the human speech production system . . . . . . . . . . . . . . . . . . . 7 2.2. Vowel sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3. Consonantal sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4. Acoustic properties of speech sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5. Coarticulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.6. Phonotactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.7. Summary and implications for the design of a Silent-Speech Interface (SSI) . . . . . . . 21 3. Articulatory data acquisition techniques in Silent-Speech Interfaces 25 3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2. Scope of the literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3. Video Recordings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.4. Ultrasonography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.5. Electromyography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.6. Permanent-Magnetic Articulography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.7. Electromagnetic Articulography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.8. Radio waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.9. Palatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.10.Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4. Electro-Optical Stomatography 55 4.1. Contact sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.2. Optical distance sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.3. Lip sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.4. Sensor Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4.5. Control Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.6. Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5. Articulation-to-Text 99 5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.2. Command word recognition pilot study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.3. Command word recognition small-scale study . . . . . . . . . . . . . . . . . . . . . . . . 102 6. Articulation-to-Speech 109 6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.2. Articulatory synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.3. The six point vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 6.4. Objective evaluation of the vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . 116 6.5. Perceptual evaluation of the vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . 120 6.6. Direct synthesis using EOS to control the vocal tract model . . . . . . . . . . . . . . . . 125 6.7. Pitch and voicing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 7. Summary and outlook 145 7.1. Summary of the contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 7.2. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 A. Overview of the International Phonetic Alphabet 151 B. Mathematical proofs and derivations 153 B.1. Combinatoric calculations illustrating the reduction of possible syllables using phonotactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 B.2. Signal Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 B.3. Effect of the contact sensor area on the conductance . . . . . . . . . . . . . . . . . . . . 155 B.4. Calculation of the forward current for the OP280V diode . . . . . . . . . . . . . . . . . . 155 C. Schematics and layouts 157 C.1. Schematics of the control unit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 C.2. Layout of the control unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 C.3. Bill of materials of the control unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 C.4. Schematics of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 C.5. Layout of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 C.6. Bill of materials of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 D. Sensor unit assembly 169 E. Firmware flow and data protocol 177 F. Palate file format 181 G. Supplemental material regarding the vocal tract model 183 H. Articulation-to-Speech: Optimal hyperparameters 189 Bibliography 19

High-resolution full-vocal-tract dynamic speech magnetic resonance imaging

Dynamic magnetic resonance imaging (MRI) holds great promise for speech-related studies because of its potential to investigate velopharyngeal motion and physiological properties jointly in real time. However, many applications of dynamic speech MRI are limited by the technical trade-offs in imaging speed, spatial coverage, spatial resolution and clinical interpretation. In particular, high-resolution dynamic speech MRI with full-vocal-tract coverage and phonetically meaningful interpretation remains a challenging goal for many speech researchers. This dissertation develops novel model-based dynamic speech MRI approaches to enable high-resolution, full-vocal-tract 3D dynamic speech MRI with quantitative characterization of the articulatory motion. Our approaches include technical developments in imaging models, data acquisition strategies and image reconstruction methods: (a) high-spatiotemporal-resolution speech MRI from sparsely sampled data is achieved by employing a low-rank imaging model that leverages the spatiotemporal correlations in dynamic speech motion; (b) a self-navigated sampling strategy is developed and employed to acquire spatiotemporal data at high imaging speed, which collects high-nominal-frame-rate cone navigators and randomized Cartesian imaging data within a single TR; (c) quantitative interpretation of speech motion is enabled by introducing a deformation-based sparsity constraint that not only improves image reconstruction quality but also analyzes articulatory motion by a high-resolution deformation field; and (d) accurate assessment of subject-specific motion as opposed to generic motion pattern is realized by using a low-rank plus sparse imaging model jointly with a technique to construct high-quality spatiotemporal atlas. Regional sparse modeling is further introduced to assist effective motion analysis in the regions of interest. Our approaches are evaluated through both simulations on numerical phantoms and in vivo validation experiments across multiple subject groups. Both simulation and experimental results allow visualization of articulatory dynamics with a frame rate of 166 frames per second, a spatial resolution of 2.2 mm x 2.2 mm x 5.0 mm, and a spatial coverage of 280 mm x 280 mm x 40 mm covering the entire upper vocal tract across 8 mid-sagittal slices. Deformation fields yielded from our approaches share an identical spatiotemporal resolution that characterizes accurate soft-tissue motion. With a high-quality atlas, the low-rank and the sparse components are reconstructed to reveal both subject-specific motion and generic speech motion across a specific subject group. The effectiveness of our approaches is demonstrated through practical phonetics investigations that include (a) integrative imaging and acoustics analysis of velopharyngeal closure; (b) understanding the formation and variation in a variety of languages, American English, North Metropolitan French, Brazilian Portuguese and Levantine Arabic; and (c) analyzing motion variability of a particular subject with respect to a specific subject group. The capabilities of our method have the potential for precise assessment of the oropharyngeal dynamics and comprehensive evaluation of speech motion