We describe a minimally-supervised method for computing a statistical shape space model of the palate surface. The model is created from a corpus of volumetric magnetic resonance imaging (MRI) scans collected from 12 speakers. We extract a 3D mesh of the palate from each speaker, then train the model using principal component analysis (PCA). The palate model is then tested using 3D MRI from another corpus and evaluated using a high-resolution optical scan. We find that the error is low even when only a handful of measured coordinates are available. In both cases, our approach yields promising results. It can be applied to extract the palate shape from MRI data, and could be useful to other analysis modalities, such as electromagnetic articulography (EMA) and ultrasound tongue imaging (UTI).
@cite_11 used realtime mri to investigate the morphological variation of the palate and the posterior pharyngeal wall. They extracted the shape information from mid-sagittal slices of the vocal tract. Afterwards, they applied a pca to the obtained data to extract the principal modes of variation of both structures. In their study, they found that the obtained principal modes could actually be related to anatomical variation, such as the degree of concavity of the palate. However, this study was restricted to the 2D case.
@cite_11
2127211243 2063036810 2145442746 2336208919
Abstract This paper examines the degrees of correlation among vocal-tract and facial movement data and the speech acoustics. Multilinear techniques are applied to support the claims that facial motion during speech is largely a by-product of producing the speech acoustics and further that the spectral envelope of the speech acoustics can be better estimated by the 3D motion of the face than by the midsagittal motion of the anterior vocal-tract (lips, tongue and jaw). Experimental data include measurements of the motion of markers placed on the face and in the vocal-tract, as well as the speech acoustics, for two subjects. The numerical results obtained show that, for both subjects, 91 of the total variance observed in the facial motion data could be determined from vocal-tract motion by means of simple linear estimators. For the inverse path, i.e. recovery of vocal-tract motion from facial motion, the results indicate that about 80 of the variance observed in the vocal-tract can be estimated from the face. Regarding the speech acoustics, it is observed that, in spite of the nonlinear relation between vocal-tract geometry and acoustics, linear estimators are sufficient to determine between 72 and 85 (depending on subject and utterance) of the variance observed in the RMS amplitude and LSP parametric representation of the spectral envelope. A dimensionality analysis is also carried out, and shows that between four and eight components are sufficient to represent the mappings examined. Finally, it is shown that even the tongue, which is an articulator not necessarily coupled with the face, can be recovered reasonably well from facial motion since it frequently displays the same kind of temporal pattern as the jaw during speech. Canonical correlation analysis (CCA) and kernel CCA can be used for unsupervised learning of acoustic features when a second view (e.g., articulatory measurements) is available for some training data, and such projections have been used to improve phonetic frame classification. Here we study the behavior of CCA-based acoustic features on the task of phonetic recognition, and investigate to what extent they are speaker-independent or domain-independent. The acoustic features are learned using data drawn from the University of Wisconsin X-ray Microbeam Database (XRMB). The features are evaluated within and across speakers on XRMB data, as well as on out-of-domain TIMIT and MOCHA-TIMIT data. Experimental results show consistent improvement with the learned acoustic features over baseline MFCCs and PCA projections. In both speaker-dependent and cross-speaker experiments, phonetic error rates are improved by 4-9 absolute (10-23 relative) using CCA-based features over baseline MFCCs. In cross-domain phonetic recognition (training on XRMB and testing on MOCHA or TIMIT), the learned projections provide smaller improvements. A machine learning technique is used to match reconstructed tongue contours in 30 frame per second ultrasound images to speaker vocal tract parameters obtained from a synchronized audio track. Speech synthesized using the learned parameters and noise as an activation function displays many of the time and frequency domain characteristics of the original audio, and, for isolated passages, is remarkably clear - although no articulators other than the tongue are included. The combination of positron emission tomography (PET) and magnetic resonance imaging (MRI) offers unique possibilities. In this paper we aim to exploit the high spatial resolution of MRI to enhance the reconstruction of simultaneously acquired PET data. We propose a new prior to incorporate structural side information into a maximum a posteriori reconstruction. The new prior combines the strengths of previously proposed priors for the same problem: it is very efficient in guiding the reconstruction at edges available from the side information and it reduces locally to edge-preserving total variation in the degenerate case when no structural information is available. In addition, this prior is segmentation-free, convex and no a priori assumptions are made on the correlation of edge directions of the PET and MRI images. We present results for a simulated brain phantom and for real data acquired by the Siemens Biograph mMRR for a hardware phantom and a clinical scan. The results from simulations show that the new prior has a better trade-off between enhancing common anatomical boundaries and preserving unique features than several other priors. Moreover, it has a better mean absolute biasto- mean standard deviation trade-off and yields reconstructions with superior relative 2-error and structural similarity index. These findings are underpinned by the real data results from a hardware phantom and a clinical patient confirming that the new prior is capable of promoting well-defined anatomical boundaries.