Gotowa bibliografia na temat „Articulatory data”

Technologies, such as real-time magnetic resonance (RT-MRI), can provide valuable information to evolve our understanding of the static and dynamic aspects of speech by contributing to the determination of which articulators are essential (critical) in producing specific sounds and how (gestures). While a visual analysis and comparison of imaging data or vocal tract profiles can already provide relevant findings, the sheer amount of available data demands and can strongly profit from unsupervised data-driven approaches. Recent work, in this regard, has asserted the possibility of determining critical articulators from RT-MRI data by considering a representation of vocal tract configurations based on landmarks placed on the tongue, lips, and velum, yielding meaningful results for European Portuguese (EP). Advancing this previous work to obtain a characterization of EP sounds grounded on Articulatory Phonology, important to explore critical gestures and advance, for example, articulatory speech synthesis, entails the consideration of a novel set of tract variables. To this end, this article explores critical variable determination considering a vocal tract representation aligned with Articulatory Phonology and the Task Dynamics framework. The overall results, obtained considering data for three EP speakers, show the applicability of this approach and are consistent with existing descriptions of EP sounds.

Abstract When conventional acoustic-verbal communication is neither possible or desirable, silent speech interfaces (SSI) rely on biosignals, non-acoustic signals created by the human body during speech production, to facilitate communication. Despite considerable advances in sensing techniques that can be employed to capture these biosignals, majority of them are used under controlled scenarios in laboratories. One such example is Electromagnetic Articulograph (EMA), which monitors articulatory motion. It is expensive with inconvenient wiring and practically not portable in real world. Since articulator measurement is difficult, articulatory parameters may be estimated from acoustics through inversion. Acoustic-to-articulatory inversion (AAI) is a technique for determining articulatory parameters using acoustic input. Automatic voice recognition, text-to- speech synthesis, and speech accent conversion can all benefit from this. However, for speakers with no articulatory data, inversion is required in many practical applications. Articulatory reconstruction is more useful when the inversion is speaker independent. Initially, we analysed positional data to better understand the relationship between sensor data and uttered speech. Following the analysis, we built a speaker independent articulatory reconstruction system that uses a Bi- LSTM model. Additionally, we evaluated the trained model using standard evaluation measures.

We have argued that dynamically defined articulatory gestures are the appropriate units to serve as the atoms of phonological representation. Gestures are a natural unit, not only because they involve task-oriented movements of the articulators, but because they arguably emerge as prelinguistic discrete units of action in infants. The use of gestures, rather than constellations of gestures as in Root nodes, as basic units of description makes it possible to characterise a variety of language patterns in which gestural organisation varies. Such patterns range from the misorderings of disordered speech through phonological rules involving gestural overlap and deletion to historical changes in which the overlap of gestures provides a crucial explanatory element.Gestures can participate in language patterns involving overlap because they are spatiotemporal in nature and therefore have internal duration. In addition, gestures differ from current theories of feature geometry by including the constriction degree as an inherent part of the gesture. Since the gestural constrictions occur in the vocal tract, which can be charactensed in terms of tube geometry, all the levels of the vocal tract will be constricted, leading to a constriction degree hierarchy. The values of the constriction degree at each higher level node in the hierarchy can be predicted on the basis of the percolation principles and tube geometry. In this way, the use of gestures as atoms can be reconciled with the use of Constriction degree at various levels in the vocal tract (or feature geometry) hierarchy.The phonological notation developed for the gestural approach might usefully be incorporated, in whole or in part, into other phonologies. Five components of the notation were discussed, all derived from the basic premise that gestures are the primitive phonological unit, organised into gestural scores. These components include (1) constriction degree as a subordinate of the articulator node and (2) stiffness (duration) as a subordinate of the articulator node. That is, both CD and duration are inherent to the gesture. The gestures are arranged in gestural scores using (3) articulatory tiers, with (4) the relevant geometry (articulatory, tube or feature) indicated to the left of the score and (5) structural information above the score, if desired. Association lines can also be used to indicate how the gestures are combined into phonological units. Thus, gestures can serve both as characterisations of articulatory movement data and as the atoms of phonological representation.

Purpose To quantify the articulatory distinctiveness of 8 major English vowels and 11 English consonants based on tongue and lip movement time series data using a data-driven approach. Method Tongue and lip movements of 8 vowels and 11 consonants from 10 healthy talkers were collected. First, classification accuracies were obtained using 2 complementary approaches: (a) Procrustes analysis and (b) a support vector machine. Procrustes distance was then used to measure the articulatory distinctiveness among vowels and consonants. Finally, the distance (distinctiveness) matrices of different vowel pairs and consonant pairs were used to derive articulatory vowel and consonant spaces using multidimensional scaling. Results Vowel classification accuracies of 91.67% and 89.05% and consonant classification accuracies of 91.37% and 88.94% were obtained using Procrustes analysis and a support vector machine, respectively. Articulatory vowel and consonant spaces were derived based on the pairwise Procrustes distances. Conclusions The articulatory vowel space derived in this study resembled the long-standing descriptive articulatory vowel space defined by tongue height and advancement. The articulatory consonant space was consistent with feature-based classification of English consonants. The derived articulatory vowel and consonant spaces may have clinical implications, including serving as an objective measure of the severity of articulatory impairment.

This study pursued two goals: (1) to establish range of motion (ROM) demand tiers (i.e., low, moderate, high) specific to the jaw (J), lower lip (LL), posterior tongue (PT), and anterior tongue (AT) for multisyllabic words based on the articulatory performance of neurotypical talkers and (2) to identify demand- and disease-specific articulatory performance characteristics in talkers with amyotrophic lateral sclerosis (ALS) and Parkinson’s disease (PD). J, LL, PT, and AT movements of 12 talkers with ALS, 12 talkers with PD, and 12 controls were recorded using electromagnetic articulography. Vertical ROM, average speed, and movement duration were measured. Results showed that in talkers with PD, J and LL ROM were already significantly reduced at the lowest tier whereas PT and AT ROM were only significantly reduced at moderate and high tiers. In talkers with ALS, J ROM was significantly reduced at the moderate tier whereas LL, PT, and AT ROM were only significantly reduced at the highest tier. In both clinical groups, significantly reduced J and LL speeds could already be observed at the lowest tier whereas significantly reduced AT speeds could only be observed at the highest tier. PT speeds were already significantly reduced at the lowest tier in the ALS group but not until the moderate tier in the PD group. Finally, movement duration, but not ROM or speed performance, differentiated between ALS and PD even at the lowest tier. Results suggest that articulatory deficits vary with stimuli-specific motor demands across articulators and clinical groups.

Purpose Dysarthria is a neuromotor speech disorder caused by neuromuscular disturbances that affect one or more articulators resulting in unintelligible speech. Though inter-phoneme articulatory variations are well captured by formant frequency-based acoustic features, these variations are expected to be much higher for dysarthric speakers than normal. These substantial variations can be well captured by placing sensors in appropriate articulatory position. This study focuses to determine a set of articulatory sensors and parameters in order to assess articulatory dysfunctions in dysarthric speech. Design/methodology/approach The current work aims to determine significant sensors and parameters associated using motion path and correlation analyzes on the TORGO database of dysarthric speech. Among eight informative sensor channels and six parameters per channel in positional data, the sensors such as tongue middle, back and tip, lower and upper lips and parameters (y, z, φ) are found to contribute significantly toward capturing the articulatory information. Acoustic and positional data analyzes are performed to validate these identified significant sensors. Furthermore, a convolutional neural network-based classifier is developed for both phone-and word-level classification of dysarthric speech using acoustic and positional data. Findings The average phone error rate is observed to be lower, up to 15.54% for positional data when compared with acoustic-only data. Further, word-level classification using a combination of both acoustic and positional information is performed to study that the positional data acquired using significant sensors will boost the performance of classification even for severe dysarthric speakers. Originality/value The proposed work shows that the significant sensors and parameters can be used to assess dysfunctions in dysarthric speech effectively. The articulatory sensor data helps in better assessment than the acoustic data even for severe dysarthric speakers.

This note describes how dynamic electropalatography (EPG) can be used for the acquisition and analysis of articulatory data. Various data reduction procedures developed to analyze the electropalatographic data are reported. Specifically, these procedures concern two interesting areas in EPG data analysis—first, the novel use of speaker-specific articulatory regions and second, the development of arithmetic indices to quantify time-varying articulatory behavior and reflect reduction and coarticulation.

Purpose The current study investigated whether articulatory kinematic patterns can be extrapolated across the spectrum of dysarthria severity in individuals with amyotrophic lateral sclerosis (ALS). Method Temporal and spatial articulatory kinematic data were collected using electromagnetic articulography from 14 individuals with dysarthria secondary to ALS and 6 typically aging speakers. Speech intelligibility and speaking rate were used as indices of severity. Results Temporal measures (duration, speed of articulators) were significantly correlated with both indices of severity. In speakers with dysarthria, spatial measures were not correlated with severity except in 3 measures: tongue movement displacement was more reduced in the anterior–posterior dimension; jaw movement distance was greater in the inferior–superior dimension; jaw convex hull area was larger in speakers with slower speaking rates. Visual inspection of movement trajectories revealed that overall spatial kinematic characteristics in speakers with severe dysarthria differed qualitatively from those in speakers with mild or moderate dysarthria. Unlike speakers with dysarthria, typically aging speakers displayed variable tongue movement and minimal jaw movement. Conclusions The current study revealed that spatial articulatory characteristics, unlike temporal characteristics, showed a complicated pattern across the severity spectrum. The findings suggest that articulatory characteristics in speakers with severe dysarthria cannot simply be extrapolated from those in speakers with mild-to-moderate dysarthria secondary to ALS.