Magnetic resonance imaging of the vocal tract: techniques and applications

Magnetic resonance (MR) imaging has been used to analyse and evaluate the vocal tract shape through different techniques and with promising results in several fields. Our purpose is to demonstrate the relevance of MR and image processing for the vocal tract study. The extraction of contours of the air cavities allowed the set-up of a number of 3D reconstruction image stacks by means of the combination of orthogonally oriented sets of slices for each articulatory gesture, as a new approach to solve the expected spatial under sampling of the imaging process. In result these models give improved information for the visualization of morphologic and anatomical aspects and are useful for partial measurements of the vocal tract shape in different situations. Potential use can be found in Medical and therapeutic applications as well as in acoustic articulatory speech modelling

Segmentation and 3D reconstruction of the vocal tract from MR images - a comparative study

Speech production is an important human function involving a set of organs with specific morphological and dynamic aspects. The inter-speaker variability, the coarticulation or the nasality are some interesting aspects to improve a realistic 3D modeling of the vocal tract. For this, the understanding of the mechanism of speech production is crucial, as the current image data is not sufficient to reproduce truthfully the speakers anatomy and articulation. Hence, the goal of 3D modeling is to generate the complete geometrical and dynamical information concerning the vocal tract from medical images, such as from magnetic reso-nance imaging (MRI). This work aims to describe and compare two different segmentation techniques to at-tain the 3D shape of the vocal tract during speech production from MR images: the former based on manual tracing of the vocal tract contours and the latter based on image thresholding. Thus, the segmented cross-sectional areas were measured, and 3D models were built from the sagittal data by blending the contours ob-tained from the two segmentation techniques. The mean error of the measures computed were low for both segmentation techniques, which let us conclude that the techniques are useful to evaluate the vocal tract ge-ometry accurately. Additionally, the 3D models built using both segmentation techniques were also very similar and truthful. However, when the coronal data was used, various difficulties occurred

Magnetic resonance imaging of the vocal tract: techniques and applications

Magnetic resonance (MR) imaging has been used to analyse and evaluate the vocal tract shape through different techniques and with promising results in several fields. Our purpose is to demonstrate the relevance of MR and image processing for the vocal tract study. The extraction of contours of the air cavities allowed the set - up of a number of 3D reconstruction image stacks by means of the combination of orthogonally oriented sets of slices for e ach articulatory gesture, as a new approach to solve the expected spatial under sampling of the imaging process. In result these models give improved information for the visualization of morphologic and anatomical aspects and are useful for partial measure ments of the vocal tract shape in different situations. Potential use can be found in Medical and therapeutic applications as well as in acoustic articulatory speech modelling

Diphthong Synthesis Using the Dynamic 3D Digital Waveguide Mesh

Articulatory speech synthesis has the potential to offer more natural sounding synthetic speech than established concatenative or parametric synthesis methods. Time-domain acoustic models are particularly suited to the dynamic nature of the speech signal, and recent work has demonstrated the potential of dynamic vocal tract models that accurately reproduce the vocal tract geometry. This paper presents a dynamic 3D digital waveguide mesh (DWM) vocal tract model, capable of movement to produce diphthongs. The technique is compared to existing dynamic 2D and static 3D DWM models, for both monophthongs and diphthongs. The results indicate that the proposed model provides improved formant accuracy over existing DWM vocal tract models. Furthermore, the computational requirements of the proposed method are significantly lower than those of comparable dynamic simulation techniques. This paper represents another step toward a fully functional articulatory vocal tract model which will lead to more natural speech synthesis systems for use across society

An open-source toolbox for measuring vocal tract shape from real-time magnetic resonance images

Real-time magnetic resonance imaging (rtMRI) is a technique that provides high-contrast videographic data of human anatomy in motion. Applied to the vocal tract, it is a powerful method for capturing the dynamics of speech and other vocal behaviours by imaging structures internal to the mouth and throat. These images provide a means of studying the physiological basis for speech, singing, expressions of emotion, and swallowing that are otherwise not accessible for external observation. However, taking quantitative measurements from these images is notoriously difficult. We introduce a signal processing pipeline that produces outlines of the vocal tract from the lips to the larynx as a quantification of the dynamic morphology of the vocal tract. Our approach performs simple tissue classification, but constrained to a researcher-specified region of interest. This combination facilitates feature extraction while retaining the domain-specific expertise of a human analyst. We demonstrate that this pipeline generalises well across datasets covering behaviours such as speech, vocal size exaggeration, laughter, and whistling, as well as producing reliable outcomes across analysts, particularly among users with domain-specific expertise. With this article, we make this pipeline available for immediate use by the research community, and further suggest that it may contribute to the continued development of fully automated methods based on deep learning algorithms

A multilinear tongue model derived from speech related MRI data of the human vocal tract

We present a multilinear statistical model of the human tongue that captures anatomical and tongue pose related shape variations separately. The model is derived from 3D magnetic resonance imaging data of 11 speakers sustaining speech related vocal tract configurations. The extraction is performed by using a minimally supervised method that uses as basis an image segmentation approach and a template fitting technique. Furthermore, it uses image denoising to deal with possibly corrupt data, palate surface information reconstruction to handle palatal tongue contacts, and a bootstrap strategy to refine the obtained shapes. Our evaluation concludes that limiting the degrees of freedom for the anatomical and speech related variations to 5 and 4, respectively, produces a model that can reliably register unknown data while avoiding overfitting effects. Furthermore, we show that it can be used to generate a plausible tongue animation by tracking sparse motion capture data