Artimate: an articulatory animation framework for audiovisual speech synthesis

We present a modular framework for articulatory animation synthesis using speech motion capture data obtained with electromagnetic articulography (EMA). Adapting a skeletal animation approach, the articulatory motion data is applied to a three-dimensional (3D) model of the vocal tract, creating a portable resource that can be integrated in an audiovisual (AV) speech synthesis platform to provide realistic animation of the tongue and teeth for a virtual character. The framework also provides an interface to articulatory animation synthesis, as well as an example application to illustrate its use with a 3D game engine. We rely on cross-platform, open-source software and open standards to provide a lightweight, accessible, and portable workflow.Comment: Workshop on Innovation and Applications in Speech Technology (2012

Mage - Reactive articulatory feature control of HMM-based parametric speech synthesis

In this paper, we present the integration of articulatory control into MAGE, a framework for realtime and interactive (reactive) parametric speech synthesis using hidden Markov models (HMMs). MAGE is based on the speech synthesis engine from HTS and uses acoustic features (spectrum and f0) to model and synthesize speech. In this work, we replace the standard acoustic models with models combining acoustic and articulatory features, such as tongue, lips and jaw positions. We then use feature-space-switched articulatory-to-acoustic regression matrices to enable us to control the spectral acoustic features by manipulating the articulatory features. Combining this synthesis model with MAGE allows us to interactively and intuitively modify phones synthesized in real time, for example transforming one phone into another, by controlling the configuration of the articulators in a visual display. Index Terms: speech synthesis, reactive, articulators 1

Ultrax:An Animated Midsagittal Vocal Tract Display for Speech Therapy

Speech sound disorders (SSD) are the most common communication impairment in childhood, and can hamper social development and learning. Current speech therapy interventions rely predominantly on the auditory skills of the child, as little technology is available to assist in diagnosis and therapy of SSDs. Realtime visualisation of tongue movements has the potential to bring enormous benefit to speech therapy. Ultrasound scanning offers this possibility, although its display may be hard to interpret. Our ultimate goal is to exploit ultrasound to track tongue movement, while displaying a simplified, diagrammatic vocal tract that is easier for the user to interpret. In this paper, we outline a general approach to this problem, combining a latent space model with a dimensionality reducing model of vocal tract shapes. We assess the feasibility of this approach using magnetic resonance imaging (MRI) scans to train a model of vocal tract shapes, which is animated using electromagnetic articulography (EMA) data from the same speaker. Index Terms: Ultrasound, speech therapy, vocal tract visualisation 1

Towards Automatic Speech Identification from Vocal Tract Shape Dynamics in Real-time MRI

Vocal tract configurations play a vital role in generating distinguishable speech sounds, by modulating the airflow and creating different resonant cavities in speech production. They contain abundant information that can be utilized to better understand the underlying speech production mechanism. As a step towards automatic mapping of vocal tract shape geometry to acoustics, this paper employs effective video action recognition techniques, like Long-term Recurrent Convolutional Networks (LRCN) models, to identify different vowel-consonant-vowel (VCV) sequences from dynamic shaping of the vocal tract. Such a model typically combines a CNN based deep hierarchical visual feature extractor with Recurrent Networks, that ideally makes the network spatio-temporally deep enough to learn the sequential dynamics of a short video clip for video classification tasks. We use a database consisting of 2D real-time MRI of vocal tract shaping during VCV utterances by 17 speakers. The comparative performances of this class of algorithms under various parameter settings and for various classification tasks are discussed. Interestingly, the results show a marked difference in the model performance in the context of speech classification with respect to generic sequence or video classification tasks.Comment: To appear in the INTERSPEECH 2018 Proceeding

Articulatory Tradeoffs Reduce Acoustic Variability During American English /r/ Production

Acoustic and articulatory recordings reveal that speakers utilize systematic articulatory tradeoffs to maintain acoustic stability when producing the phoneme /r/. Distinct articulator configurations used to produce /r/ in various phonetic contexts show systematic tradeoffs between the cross-sectional areas of different vocal tract sections. Analysis of acoustic and articulatory variabilities reveals that these tradeoffs act to reduce acoustic variability, thus allowing large contextual variations in vocal tract shape; these contextual variations in turn apparently reduce the amount of articulatory movement required. These findings contrast with the widely held view that speaking involves a canonical vocal tract shape target for each phoneme.National Institute on Deafness and Other Communication Disorders (1R29-DC02852-02, 5R01-DC01925-04, 1R03-C2576-0l); National Science Foundation (IRI-9310518

Real-time dynamic articulations in the 2-D waveguide mesh vocal tract model

Time domain articulatory vocal tract modeling in one-dimensional (1-D) is well established. Previous studies into two-dimensional (2-D) simulation of wave propagation in the vocal tract have shown it to present accurate static vowel synthesis. However, little has been done to demonstrate how such a model might accommodate the dynamic tract shape changes necessary in modeling speech. Two methods of applying the area function to the 2-D digital waveguide mesh vocal tract model are presented here. First, a method based on mapping the cross-sectional area onto the number of waveguides across the mesh, termed a widthwise mapping approach is detailed. Discontinuity problems associated with the dynamic manipulation of the model are highlighted. Second, a new method is examined that uses a static-shaped rectangular mesh with the area function translated into an impedance map which is then applied to each waveguide. Two approaches for constructing such a map are demonstrated; one using a linear impedance increase to model a constriction to the tract and another using a raised cosine function. Recommendations are made towards the use of the cosine method as it allows for a wider central propagational channel. It is also shown that this impedance mapping approach allows for stable dynamic shape changes and also permits a reduction in sampling frequency leading to real-time interaction with the model

Three-dimensional linear modeling of tongue: articulatory data and models

Volume images of tongue were acquired by MRI from one subject uttering a corpus representative of French allophone articulations. Supplementary images of hard palate, jaw, and hyoid bone were acquired by CT. The three-dimensional tongue surface outline was represented, for each of the 46 articulations of the corpus, by a mesh obtained by fitting a generic mesh to the set of tongue contours traced from the MR images. Jaw and hyoid bone positions were also determined. The set of the 3D coordinates of all vertices of the tongue mesh constituted the variables on which linear component analysis was applied. Six linearly independent components were found to explain 87 % of the variance of the tongue data. The associated parameters that control the linear articulator tongue model are related to jaw and hyoid positions, and to the actions of tongue muscles such as the genioglossus, the hyoglossus or the styloglossus. In addition, it was shown that the full 3D tongue surface is predictable from its 2D midsagittal contour with a mere 13.6 % increase in the overall full 3D reconstruction RMS error, which confirms quantitatively previous results. Finally, the tongue volume was found to depart by at most ±5% from its mean over the corpus, which supports the hypothesis of tongue tissue incompressibility for speech

A silent speech system based on permanent magnet articulography and direct synthesis

In this paper we present a silent speech interface (SSI) system aimed at restoring speech communication for individuals who have lost their voice due to laryngectomy or diseases affecting the vocal folds. In the proposed system, articulatory data captured from the lips and tongue using permanent magnet articulography (PMA) are converted into audible speech using a speaker-dependent transformation learned from simultaneous recordings of PMA and audio signals acquired before laryngectomy. The transformation is represented using a mixture of factor analysers, which is a generative model that allows us to efficiently model non-linear behaviour and perform dimensionality reduction at the same time. The learned transformation is then deployed during normal usage of the SSI to restore the acoustic speech signal associated with the captured PMA data. The proposed system is evaluated using objective quality measures and listening tests on two databases containing PMA and audio recordings for normal speakers. Results show that it is possible to reconstruct speech from articulator movements captured by an unobtrusive technique without an intermediate recognition step. The SSI is capable of producing speech of sufficient intelligibility and naturalness that the speaker is clearly identifiable, but problems remain in scaling up the process to function consistently for phonetically rich vocabularies

Three-dimensional modeling of tongue during speech using MRI data

The tongue is the most important and dynamic articulator for speech formation, because of its anatomic aspects (particularly, the large volume of this muscular organ comparatively to the surrounding organs of the vocal tract) and also due to the wide range of movements and flexibility that are involved. In speech communication research, a variety of techniques have been used for measuring the three-dimensional vocal tract shapes. More recently, magnetic resonance imaging (MRI) becomes common; mainly, because this technique allows the collection of a set of static and dynamic images that can represent the entire vocal tract along any orientation. Over the years, different anatomical organs of the vocal tract have been modelled; namely, 2D and 3D tongue models, using parametric or statistical modelling procedures. Our aims are to present and describe some 3D reconstructed models from MRI data, for one subject uttering sustained articulations of some typical Portuguese sounds. Thus, we present a 3D database of the tongue obtained by stack combinations with the subject articulating Portuguese vowels. This 3D knowledge of the speech organs could be very important; especially, for clinical purposes (for example, for the assessment of articulatory impairments followed by tongue surgery in speech rehabilitation), and also for a better understanding of acoustic theory in speech formation