Electromagnetic Articulography Sensors Research Articles

Tongue and jaw movement assessed by 3D motion capture during gum chewing.

The tongue plays an important role in mastication, swallowing, and articulation, but it cannot be directly observed because of its location inside the oral cavity. This study aimed to clarify detailed 3D tongue movements during chewing using electromagnetic articulography (EMA). The participants were 10 healthy, young volunteers (average age 26.8 ± 2.1years; 5 males, 5 females). Tongue and jaw movement during gum chewing was measured and recorded using EMA. Four EMA sensors were attached to the anterior, posterior, left, and right surfaces of the tongue, and one sensor was also attached to the mandibular left incisor. The tongue motion during the chewing cycle was spatially and sequentially analyzed based on the motion trajectories of the tongue and mandible. The tongue moved downward and to the masticatory side in a manner similar to the movement of the jaw. The anterior tongue marker moved downward to a greater extent than the other tongue markers. However, the tongue moved forward as the jaw moved backward. The anterior marker reached the most anterior position during the jaw-opening phase and the posterior markers reached the most anterior position during the jaw-closing phase. Just before maximum jaw-opening, all markers on the tongue reached the bottom lowest position. During the jaw-closing phase, the tongue reached the dominant farthest position in the masticatory side. All the markers reached the most posterior position during the occlusal phase. These findings demonstrate the sequence of tongue motion patterns during gum chewing.

Mapping palatal shape to electromagnetic articulography data: An approach using 3D scanning and sensor matching.

A method for superimposing the shape of the palate on three-dimensional (3D) electromagnetic articulography (EMA) data is proposed. A biteplate with a dental impression tray and EMA sensors is used to obtain the palatal shape and record the sensor positions. The biteplate is then 3D scanned, and the scanned palate is mapped to the EMA data by matching the sensor positions on the scanned image with those in the EMA readings. The average distance between the mapped palate and the EMA palate traces is roughly 1 mm for nine speakers and is comparable to the measurement error of the EMA.

An investigation of interference between electromagnetic articulography and electroglottography

The present study tested whether there is cross interference between state-of-the-art electromagnetic articulography (EMA) and electroglottography (EGG) during the acquisition of kinematic speech data. In Experiment 1, we calibrated EMA sensors with and without EGG electrodes present in the EMA field. In Experiment 2, EMA was used to record lip, tongue, and jaw movements for one male talker and one female talker, with and without simultaneous EGG recording. Collectively, the results provide no evidence of signal artifacts in either direction, suggesting that current EMA and EGG technology can be combined to reliably assess laryngeal and supralaryngeal motor coordination in speech.

An investigation of interference between electromagnetic articulography and electroglottography.

The present study tested whether there is cross-interference between electromagnetic articulography (EMA) and electroglottography (EGG) during the acquisition of kinematic speech data. In experiments 1A and 1B, EMA sensors were calibrated with and without EGG electrodes present in the EMA field. In experiment 2, EMA was used to record lip, tongue, and jaw movements for one male speaker and one female speaker, with and without simultaneous EGG recording. Collectively, the results provide no evidence of signal artifacts in either direction, suggesting that EMA and EGG technology can be combined to reliably assess laryngeal and supralaryngeal motor coordination in speech.

Electromagnetic articulography appears feasible for assessment of speech motor skills in cochlear-implant users.

The present investigation tested whether there is cross-interference between current electromagnetic articulography (EMA) and cochlear implants (CIs). In an initial experiment, we calibrated EMA sensors with and without a CI present in the EMA field, and measured impedances of all CI electrodes when in and out of the EMA field. In a subsequent experiment, head reference sensor positions were recorded during a speaking task for a normal-hearing talker with and without a CI present in the EMA field. Results revealed minimal interference between the devices, suggesting that EMA is a promising method for assessing speech motor skills in CI users.

Electromagnetic articulography is feasible for assessment of speech motor skills in cochlear implant users

Auditory input is essential to the acquisition of fluent speech production, and yet speech production has been scarcely studied in congenitally deaf individuals since cochlear implants (CIs) became a standard treatment. In particular, methods of directly observing speech movements have not been applied, such as electromagnetic articulography (EMA). Several EMA studies have been done with adults with CIs, but those studies used older CI andEMA technology and included subjects with adult-onset hearing-loss, who would have developed speech motor control and phonological representations before losing their hearing. We here investigated whether current EMA technology can be reliably used with CIs by testing (1) whether CIs affect the recordings obtained from EMA, and (2) whether EMA interferes with the signal processing of CIs. In an initial experiment, we calibrated EMA sensors with and without a CI present in the EMA field, and measured impedances of all CI electrodes when in and out of the EMA field. In a subsequent experiment, speech movements were recorded for a normal-hearing talker with and without a CI present in the EMA field. Results indicate that there is no cross-interference between current EMA and CIs, and that EMA data can reliably be obtained from CI users.

A review of data collection practices using electromagnetic articulography

This paper reviews data collection practices in electromagnetic articulography (EMA) studies, with a focus on sensor placement. We first introduce electromagnetic articulography as a method. We then focus on existing data collection practices. Our overview is based on a literature review of 905 publications from a large variety of journals and conferences, identified through a systematic keyword search in Google Scholar. The review shows that experimental designs vary greatly, which in turn may limit researchers’ ability to compare results across studies. Finally, we describe an EMA data collection procedure that includes an articulatory-driven strategy for determining where to position sensors on the tongue without causing discomfort to the participant. We also evaluate three approaches for preparing (NDI Wave) EMA sensors reported in the literature with respect to the duration the sensors remain attached to the tongue: 1) attaching out-of-the-box sensors, 2) attaching sensors coated in latex, and 3) attaching sensors coated in latex with an additional latex flap. Results indicate no clear general effect of sensor preparation type on adhesion duration. A subsequent exploratory analysis reveals that sensors with the additional flap tend to adhere for shorter times than the other two types, but that this pattern is inverted for the most posterior tongue sensor.

On simultaneous electromagnetic articulography and electroglottography data acquisition

Simultaneous measurements of speech articulator movement and laryngeal activity are desirable for obtaining a picture of the coarticulatory behavior between oral articulators and laryngeal vocal fold behavior. However, since electroglottography (EGG) use the application of a weak current across the larynx, which is in close proximity to the oral vocal tract, there is a possibility that its use may interfere with the sensor voltage fluctuation measurements used in electromagnetic articulography (EMA). In order to investigate any hypothetical interference effect, data was collected using Northern Digital Instruments’ Wave articulograph with Glottal Enterprises’s EG2-PCX electroglottograph. Datasets were collected from one male and one female speaker—both with simultaneous EMA and EGG and, for comparison, with EMA alone. Means and standard deviations of inter-sensor distances for static (i.e., reference) and moving (e.g., tongue tip, lip, and jaw) EMA sensor trajectories were compared across the two collection conditions for possible interference effects. Preliminary results find no discernable differences in inter-sensor distances for either static or moving EMA sensors between the with- and without-EGG conditions. If this finding is maintained for additional speakers, NDI EMA data and GE EGG signals may be collected simultaneously without adverse effects on the measurement. [Work supported by NIH.]

Optimal sensor placement in electromagnetic articulography recording for speech production study

Electromagnetic articulography (EMA) is one of the technological solutions, widely used to measure the articulatory movement useful for speech production research. EMA is typically used to track articulatory flesh points by placing sensors, often heuristically, on the key articulators including lips, jaw, tongue and velum in the mid-sagittal plane. In this work, we address the problem of optimal placement of EMA sensors by posing it as the optimal selection of points for minimizing the reconstruction error of the air-tissue boundaries in the real-time magnetic resonance imaging (rtMRI) video frames of vocal tract (VT) in the mid-sagittal plane. We propose an algorithm for optimal placement of EMA sensors using dynamic programming. Experiments are performed using rtMRI video frames for read speech from four subjects with upper and lower lips as two fixed points. One optimal sensor on the upper VT boundary is found to be at an average distance of 21.41(±25.54) mm from the velum tip. Similarly, for the lower VT boundary, one optimal sensor is found at the lower incisor at a distance of 26.37(±8.08) mm from lower lip and three optimal sensors on tongue – at tongue tip (19.93(±11.45) mm from tongue base) and 38.2(±11.52) mm and 80.51(±13.51) mm away from the tongue tip.

Inversion of speech by non-linear transformation of temporary

Electromagnetic Articulography (EMA) is a precise method for speech articulators assessment which is carried out by sensors placed mainly on the tongue. Various methods are being developed in order to avoid the assessment by EMA sensors. One of them is speech inversion. Here preliminary research on speech inversion based on dynamic time warping (DTW) method has been described. Mel-frequency cepstral coefficients (MFCC) method has been chosen as the acoustic speech signal parametrization method. Root mean square errors (RMSE) of the evaluation have been presented and discussed.

Co-registration of speech production datasets from electromagnetic articulography and real-time magnetic resonance imaging.

This paper describes a spatio-temporal registration approach for speech articulation data obtained from electromagnetic articulography (EMA) and real-time Magnetic Resonance Imaging (rtMRI). This is motivated by the potential for combining the complementary advantages of both types of data. The registration method is validated on EMA and rtMRI datasets obtained at different times, but using the same stimuli. The aligned corpus offers the advantages of high temporal resolution (from EMA) and a complete mid-sagittal view (from rtMRI). The co-registration also yields optimum placement of EMA sensors as articulatory landmarks on the magnetic resonance images, thus providing richer spatio-temporal information about articulatory dynamics.

Model-based identification of motion sensor placement for tracking retraction and elongation of the tongue

Electromagnetic articulography (EMA) is designed to track facial and tongue movements. In practice, the EMA sensors for tracking the movement of the tongue's surface are placed heuristically. No recommendation exists. Within this paper, a model-based approach providing a mathematical analysis and a computational-based recommendation for the placement of sensors, which is based on the tongue's envelope of movement, is proposed. For this purpose, an anatomically detailed Finite Element (FE) model of the tongue has been employed to determine the envelope of motion for retraction and elongation using a forward simulation. Two optimality criteria have been proposed to identify a set of optimal sensor locations based on the pre-computed envelope of motion. The first one is based on the assumption that locations exhibiting large displacements contain the most information regarding the tongue's movement and are less susceptible to measurement errors. The second one selects sensors exhibiting each the largest displacements in the anterior-posterior, superior-inferior, medial-lateral and overall direction. The quality of the two optimality criteria is analysed based on their ability to deduce from the respective sensor locations the corresponding muscle activation parameters of the relevant muscle fibre groups during retraction and elongation by solving the corresponding inverse problem. For this purpose, a statistical analysis has been carried out, in which sensor locations for two different modes of deformation have been subjected to typical measurement errors. Then, for tongue retraction and elongation, the expectation value, the standard deviation, the averaged bias and the averaged coefficient of variation have been computed based on 41 different error-afflicted sensor locations. The results show that the first optimality criteria is superior to the second one and that the averaged bias and averaged coefficient of variation decrease when the number of sensors is increased from 2, 4 to 6 deployable sensors.

Evaluation of the measurement precision in three-dimensional Electromagnetic Articulography (Carstens AG500)

Three-dimensional Electromagnetic Articulography (EMA) measures location and orientation of the moving speech articulators in real time by means of small, wired sensors. We evaluated the measurement accuracy of the Carstens AG500 EMA system using data acquired simultaneously with the Vicon optical motion tracking system (OPT). EMA sensors and OPT markers were combined in a single rigid object to be able to predict location and orientation of the EMA sensors from OPT motion tracking data. The error was computed as the root mean squared (RMS) error. We found that deviations from constant inter-sensor distances (relative error) were in general below 1mm and 0.6° while the difference between the measured and estimated positions (absolute error) ranged between 1 and 2mm and 0.5° and 0.7°. By examining error patterns, four critical orientation regions were detected, but no discernible location dependent error patterning. Sensor velocity appeared to have little impact. The RMS error of the original position calculation has not been found to be a reliable predictor. In the absence of a clear error structure we recommend careful analysis of unexpected findings in speech production data acquired with EMA. Avenues for further improvement of the system are discussed.

Studies of Articulatory Feedback Treatment for Apraxia of Speech Based on Electromagnetic Articulography

Electromagnetic articulography (EMA) is a method originally designed for the laboratory measurement of speech articulatory motion (Schönle et al., 1987). We describe a novel use of this technology applied to the remediation of apraxia of speech (AOS). In this experimental technique, individuals with AOS are provided with real-time, visual information concerning the movement of the tongue during speech. From information sent via EMA sensors mounted on the tongue, patients are guided into hitting “targets” displayed on a computer monitor, designed to guide correct articulatory placement. The results of several studies suggest that augmented feedback-based treatment is efficacious and that this treatment follows principles of motor learning described in the limb motor literature. Potential challenges facing this type of approach, as well as some new directions, are discussed.