Directions Into Velocities Of Articulators Research Articles

On the interplay between speech perception and production: insights from research and theories

The study of spoken communication has long been entrenched in a debate surrounding the interdependence of speech production and perception. This mini review summarizes findings from prior studies to elucidate the reciprocal relationships between speech production and perception. We also discuss key theoretical perspectives relevant to speech perception-production loop, including hyper-articulation and hypo-articulation (H&H) theory, speech motor theory, direct realism theory, articulatory phonology, the Directions into Velocities of Articulators (DIVA) and Gradient Order DIVA (GODIVA) models, and predictive coding. Building on prior findings, we propose a revised auditory-motor integration model of speech and provide insights for future research in speech perception and production, focusing on the effects of impaired peripheral auditory systems.

Associations between Age and Cortical Thickness in Brain Regions Supporting Speech Motor Control

AbstractBackgroundPrior research has demonstrated consistent associations between age and cortical thickness, as well as longitudinal reductions in cortical thickness in older adults (e.g., Salat et al., 2004; Storsve et al., 2014). To date, the aging of regions supporting speech production has not been directly investigated. Such information is, however, needed to differentiate age‐related cortical atrophy in speech regions from that occurring due to underlying neurodegenerative diseases. We examined cross‐sectional associations between age and cortical thickness of brain regions supporting speech motor control. These areas include subregions of the premotor cortex, primary motor area, supplementary motor area, pars opercularis, posterior inferior frontal sulcus, and insula as defined by the Directions Into Velocities of Articulators (DIVA) model ‐ a model for neural representation and mechanisms of speech motor control (Tourville & Guenther, 2011).MethodThe data were obtained from the Alzheimer’s Disease Neuroimaging Initiative GO/2 (ADNI). Participants included 163 (77 M, 86 F) cognitively intact adults aged 56‐89 years (Mean = 73.45, SD = 6.23). All participants were right‐handed and had at least 12 years of education (Mean = 16.53, SD = 2.55). Whole‐brain T1‐weighted MRI images were acquired by a 3‐Tesla Siemens scanner. FreeSurfer software was used for cortical surface reconstruction and to extract the cortical thickness of speech regions as identified by the SpeechLabel Parcellation System (Cai et al., 2014) developed based on the DIVA model.ResultSignificant associations between age and cortical thickness was observed in subregions of left premotor cortex, primary motor area, supplementary motor area, and presupplementary motor area with more limited effects in the right hemisphere. Cortical thickness of pars opercularis, posterior inferior frontal sulcus, and insula did not show significant changes over the aging process. Cross‐sectional interactions were found with regions in the left hemisphere being thicker at younger ages and having a larger negative age slope compared to their corresponding regions in the right hemisphere.ConclusionThe findings of the study suggest that certain speech‐related regions show atrophy with aging, however, these changes are not uniform. In addition, laterality in cortical thickness of speech regions at younger ages was observed to diminish over the normal aging process.

A Modeling-Guided Case Study of Disordered Speech in Minimally Verbal Children With Autism Spectrum Disorder.

Purpose Understanding what limits speech development in minimally verbal (MV) children with autism spectrum disorder (ASD) is important for providing highly effective targeted therapies. This preliminary investigation explores the extent to which developmental speech deficits predicted by Directions Into Velocities of Articulators (DIVA), a computational model of speech production, exemplify real phenotypes. Method Implementing a motor speech disorder in DIVA predicted that speech would become highly variable within and between tokens, while implementing a motor speech plus an auditory processing disorder predicted that DIVA's speech would become highly centralized (schwa-like). Acoustic analyses of DIVA's output predicted that acoustically measured phoneme distortion would be similar between the two cases, but that in the former case, speech would show more within- and between-token variability than in the latter case. We tested these predictions quantitatively on the speech of children with MV ASD. In Study 1, we tested the qualitative predictions using perceptual analysis methods. Speech pathologists blinded to the purpose of the study tallied the signs of childhood apraxia of speech that appeared in the speech of 38 MV children with ASD. K-means clustering was used to create two clusters from the group of 38, and analysis of variance was used to determine whether the clusters differed according to perceptual features corresponding to within- and between-token variability. In Study 2, we employed acoustic analyses on the speech of the child from each cluster who produced the largest number of analyzable tokens to test the predictions of differences in within-token variability, between-token variability, and vowel space area. Results Clusters produced by k-means analysis differed by perceptual features that corresponded to within-token variability. Nonsignificant differences between clusters were found for features corresponding to between-token variability. Subsequent acoustic analyses of the selected cases revealed that the speech of the child from the high-variability cluster showed significantly more quantitative within- and between-token variability than the speech of the child from the low-variability cluster. The vowel space of the child from the low-variability cluster was more centralized than that of typical children and that of the child from the high-variability cluster. Conclusions Results provide preliminary evidence that subphenotypes of children with MV ASD may exist, characterized by (a) comorbid motor speech disorder and (b) comorbid motor speech plus auditory processing disorder. The results motivate testable predictions about how these comorbidities affect speech. Supplemental Material https://doi.org/10.23641/asha.14384432.

Noninvasive neurostimulation of left ventral motor cortex enhances sensorimotor adaptation in speech production

Sensorimotor adaptation—enduring changes to motor commands due to sensory feedback—allows speakers to match their articulations to intended speech acoustics. How the brain integrates auditory feedback to modify speech motor commands and what limits the degree of these modifications remain unknown. Here, we investigated the role of speech motor cortex in modifying stored speech motor plans. In a within-subjects design, participants underwent separate sessions of sham and anodal transcranial direct current stimulation (tDCS) over speech motor cortex while speaking and receiving altered auditory feedback of the first formant. Anodal tDCS increased the rate of sensorimotor adaptation for feedback perturbation. Computational modeling of our results using the Directions Into Velocities of Articulators (DIVA) framework of speech production suggested that tDCS primarily affected behavior by increasing the feedforward learning rate. This study demonstrates how focal noninvasive neurostimulation can enhance the integration of auditory feedback into speech motor plans.

Modelling speech motor programming and apraxia of speech in the DIVA/GODIVA neurocomputational framework

ABSTRACT Background: The Directions Into Velocities of Articulators (DIVA) model and its partner, the Gradient Order DIVA (GODIVA) model, provide neurobiologically grounded, computational accounts of speech motor control and motor sequencing, with applications for the study and treatment of neurological motor speech disorders. Aims: In this review, we provide an overview of the DIVA and GODIVA models and how they explain the interface between phonological and motor planning systems to build on previous models and provide a mechanistic accounting of apraxia of speech (AOS), a disorder of speech motor programming. Main Contribution: Combined, the DIVA and GODIVA models account for both the segmental and suprasegmental features that define AOS via damage to (i) a speech sound map, hypothesized to reside in the left ventral premotor cortex, (ii) a phonological content buffer hypothesized to reside in the left posterior inferior frontal sulcus, and/or (iii) the axonal projections between these regions. This account is in line with a large body of behavioural work, and it unifies several prior theoretical accounts of AOS. Conclusions: The DIVA and GODIVA models provide an integrated framework for the generation and testing of both behavioural and neuroimaging hypotheses about the underlying neural mechanisms responsible for motor programming in typical speakers and in speakers with AOS.

Involvement of the Cortico-Basal Ganglia-Thalamocortical Loop in Developmental Stuttering.

Stuttering is a complex neurodevelopmental disorder that has to date eluded a clear explication of its pathophysiological bases. In this review, we utilize the Directions Into Velocities of Articulators (DIVA) neurocomputational modeling framework to mechanistically interpret relevant findings from the behavioral and neurological literatures on stuttering. Within this theoretical framework, we propose that the primary impairment underlying stuttering behavior is malfunction in the cortico-basal ganglia-thalamocortical (hereafter, cortico-BG) loop that is responsible for initiating speech motor programs. This theoretical perspective predicts three possible loci of impaired neural processing within the cortico-BG loop that could lead to stuttering behaviors: impairment within the basal ganglia proper; impairment of axonal projections between cerebral cortex, basal ganglia, and thalamus; and impairment in cortical processing. These theoretical perspectives are presented in detail, followed by a review of empirical data that make reference to these three possibilities. We also highlight any differences that are present in the literature based on examining adults versus children, which give important insights into potential core deficits associated with stuttering versus compensatory changes that occur in the brain as a result of having stuttered for many years in the case of adults who stutter. We conclude with outstanding questions in the field and promising areas for future studies that have the potential to further advance mechanistic understanding of neural deficits underlying persistent developmental stuttering.

Testing hypotheses about the underlying deficit of apraxia of speech through computational neural modelling with the DIVA model

Purpose: A recent behavioural experiment featuring a noise masking paradigm suggests that Apraxia of Speech (AOS) reflects a disruption of feedforward control, whereas feedback control is spared and plays a more prominent role in achieving and maintaining segmental contrasts. The present study set out to validate the interpretation of AOS as a possible feedforward impairment using computational neural modelling with the DIVA (Directions Into Velocities of Articulators) model.Method: In a series of computational simulations with the DIVA model featuring a noise-masking paradigm mimicking the behavioural experiment, we investigated the effect of a feedforward, feedback, feedforward + feedback, and an upper motor neuron dysarthria impairment on average vowel spacing and dispersion in the production of six/bVt/speech targets.Result: The simulation results indicate that the output of the model with the simulated feedforward deficit resembled the group findings for the human speakers with AOS best.Conclusion: These results provide support to the interpretation of the human observations, corroborating the notion that AOS can be conceptualised as a deficit in feedforward control.

Control modeling toward understanding articulatory disfluency in autism spectrum disorder

Speech control models founded on principles of neuroscience have potential to create powerful clinical tools for personalized medicine and for understanding behavior. We explore two prominent speech control models: Directions into Velocities of Articulators (DIVA) (Guenther, 2006) and Task Dynamics (TD) (Saltzman, 1991) toward an understanding of speech disfluency of autism spectrum disorder (ASD). To introduce perturbations into both systems in the common framework of (Parrrell, 2018), we translate the TD model into MATLAB Simulink. For word stimuli, we compare both models’ acoustic outputs, under the hypothesis that speech impediments in ASD arise from overreliance on delayed sensory feedback (Lin, 2015). For DIVA, the output from a 50ms auditory feedback delay contain increased formant production errors and formant oscillations at about 10Hz, sometimes observed in disfluent ASD cases, but also seen in stuttering (Civier, 2010). The same delay introduced in the proprioceptive and tactile feedback in TD takes 5x the time to settle at the target, creating about 20Hz oscillations in TD articulators. Though neither fully capturing disfluency in ASD, together these models lay the groundwork for a more complete neurocomputational speech control model that can be used to longitudinally track and potentially guide ASD speech therapy.

A Case of Afferent Apraxic Motor Aphasia? A Tribute to A.R.Luria

A Case of Afferent Apraxic Motor Aphasia? A Tribute to A.R.Luria

Feedforward and feedback control in apraxia of speech: effects of noise masking on vowel production.

This study was designed to test two hypotheses about apraxia of speech (AOS) derived from the Directions Into Velocities of Articulators (DIVA) model (Guenther et al., 2006): the feedforward system deficit hypothesis and the feedback system deficit hypothesis. The authors used noise masking to minimize auditory feedback during speech. Six speakers with AOS and aphasia, 4 with aphasia without AOS, and 2 groups of speakers without impairment (younger and older adults) participated. Acoustic measures of vowel contrast, variability, and duration were analyzed. Younger, but not older, speakers without impairment showed significantly reduced vowel contrast with noise masking. Relative to older controls, the AOS group showed longer vowel durations overall (regardless of masking condition) and a greater reduction in vowel contrast under masking conditions. There were no significant differences in variability. Three of the 6 speakers with AOS demonstrated the group pattern. Speakers with aphasia without AOS did not differ from controls in contrast, duration, or variability. The greater reduction in vowel contrast with masking noise for the AOS group is consistent with the feedforward system deficit hypothesis but not with the feedback system deficit hypothesis; however, effects were small and not present in all individual speakers with AOS. Theoretical implications and alternative interpretations of these findings are discussed.

Chapter 9 - Role of the auditory system in speech production

This chapter reviews evidence regarding the role of auditory perception in shaping speech output. Evidence indicates that speech movements are planned to follow auditory trajectories. This in turn is followed by a description of the Directions Into Velocities of Articulators (DIVA) model, which provides a detailed account of the role of auditory feedback in speech motor development and control. A brief description of the higher-order brain areas involved in speech sequencing (including the pre-supplementary motor area and inferior frontal sulcus) is then provided, followed by a description of the Hierarchical State Feedback Control (HSFC) model, which posits internal error detection and correction processes that can detect and correct speech production errors prior to articulation. The chapter closes with a treatment of promising future directions of research into auditory-motor interactions in speech, including the use of intracranial recording techniques such as electrocorticography in humans, the investigation of the potential roles of various large-scale brain rhythms in speech perception and production, and the development of brain-computer interfaces that use auditory feedback to allow profoundly paralyzed users to learn to produce speech using a speech synthesizer.

Research on the mechanism for phonating stressed English syllables based on DIVA model

Speech generation and acquisition is a complex cognitive process involving many brain regions. Simulating this process requires establishment of a proper neural network model according to the interaction between sensory organs and motor areas in the brain that occurs during phonation. Directions Into Velocities of Articulators (DIVA) is a neural network model of this type. In the process of speech generation and acquisition, this model can be used to control and simulate the trajectory of articulator and synthesize the desired speech. However, DIVA is flawed in terms of simulating natural language, because it cannot simulate the phonation of English stresses. A DIVA-based neural network computing model for analyzing stresses on English words is proposed. The TD-POSAL (time-domain pitch synchronous overlap add) algorithm is used to tune DIVA׳s speech and rhythm parameters in order to simulate the generation and acquisition of stresses on English words. Simulation showed that the improved DIVA model can represent the features of stressed syllables. The model׳s pitch and duration compensation effect is tested in the increased pitch perturbation experiment. The experiment demonstrates that the improved DIVA model can correctly simulate the generation and acquisition of stresses on English words.

Involuntary switching into the native language induced by electrocortical stimulation of the superior temporal gyrus: A multimodal mapping study

We describe involuntary language switching from L2 to L1 evoked by electro-stimulation in the superior temporal gyrus in a 30-year-old right-handed Serbian (L1) speaker who was also a late Italian learner (L2). The patient underwent awake brain surgery. Stimulation of other portions of the exposed cortex did not cause language switching as did not stimulation of the left inferior frontal gyrus, where we evoked a speech arrest. Stimulation effects on language switching were selective, namely, interfered with counting behaviour but not with object naming. The coordinates of the positive site were combined with functional and fibre tracking (DTI) data. Results showed that the language switching site belonged to a significant fMRI cluster in the left superior temporal gyrus/supramarginal gyrus found activated for both L1 and L2, and for both the patient and controls, and did not overlap with the inferior fronto-occipital fasciculus (IFOF), the inferior longitudinal fasciculus (ILF) and the superior longitudinal fasciculus (SLF). This area, also known as Stp, has a role in phonological processing.Language switching phenomenon we observed can be partly explained by transient dysfunction of the feed-forward control mechanism hypothesized by the DIVA (Directions Into Velocities of Articulators) model (Golfinopoulos, E., Tourville, J. A., & Guenther, F. H. (2010). The integration of large-scale neural network modeling and functional brain imaging in speech motor control. NeuroImage, 52, 862–874).

Constructing Cerebellum Model by Researching on its Contributions to DIVA

DIVA (Directions into Velocities of Articulators) is a mathematical model of the processes behind speech acquisition and production, supposed to achieve a functional representation of areas in the brain that are involved in speech production and speech perception. Introducing cerebellum control mechanism into the model plays a significant role in improving the mechanism of speech acquisition and production based on DIVA model. The paper studies its learning process, and explores cerebellar contributions to the model, that is feedforward learning, sensory predictions, feedback command production and the timing of delays, and then constructs a cerebellum model that is closer to neuroanatomy and is applied to DIVA model. Simulation results show that the improved DIVA model can produce more clear and explicit speech sounds, and is more close to human-like pronunciation system. The cerebellum model that established in this paper can be applied to speech acquisition and production based on DIVA model. DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.5388

Research on Cerebellar Contributions to Speech Acquisition and Production Based on DIVA Model

DIVA (Directions Into Velocities of Articulators) is a mathematical model of the processes behind speech acquisition and production, supposed to achieve a functional representation of areas in the brain that are involved in speech production and speech perception. Owing to its especial structure and roles, introducing cerebellum control modules into the model plays a significant role in improving the mechanism of speech acquisition and production based on DIVA model. To solve this problem, the paper studies its learning process, and explores cerebellar contributions to the model, that is feedforward learning, sensory predictions, feedback command production and the timing of delays, and then adds the corresponding cerebellum modules into the feedback control subsystem on the basis of the current model. Simulation results show that the improved DIVA model can produce more clear and explicit speech sounds, and is more close to human-like pronunciation system.

Computational Neural Modeling of Speech Motor Control in Childhood Apraxia of Speech (CAS)

Childhood apraxia of speech (CAS) has been associated with a wide variety of diagnostic descriptions and has been shown to involve different symptoms during successive stages of development. In the present study, the authors attempted to associate the symptoms of CAS in a particular developmental stage with particular information-processing deficits by using computational modeling with the Directions Into Velocities of Articulators (DIVA) model. The hypothesis was that the speech production system in CAS suffers from poor feed-forward control and, consequently, an increased reliance on the feedback control subsystem. In a series of computer simulations, the authors systematically varied the ratio between feed-forward and feedback control during production attempts in the acquisition of feed-forward motor commands. The simulations were evaluated acoustically on 4 selected key symptoms of CAS. Results showed that increasing the reliance on feedback control causes increased severity of these 4 symptoms of CAS: deviant coarticulation, speech sound distortion, searching articulation, and increased variability. The findings support the idea that the key symptoms found in CAS could result from an increased reliance on feedback control due to poor feed-forward commands. Two possible root causes of degraded feed-forward control in CAS are discussed: reduced somatosensory information and increased levels of neural noise.

The integration of large-scale neural network modeling and functional brain imaging in speech motor control

Speech production demands a number of integrated processing stages. The system must encode the speech motor programs that command movement trajectories of the articulators and monitor transient spatiotemporal variations in auditory and somatosensory feedback. Early models of this system proposed that independent neural regions perform specialized speech processes. As technology advanced, neuroimaging data revealed that the dynamic sensorimotor processes of speech require a distributed set of interacting neural regions. The DIVA (Directions into Velocities of Articulators) neurocomputational model elaborates on early theories, integrating existing data and contemporary ideologies, to provide a mechanistic account of acoustic, kinematic, and functional magnetic resonance imaging (fMRI) data on speech acquisition and production. This large-scale neural network model is composed of several interconnected components whose cell activities and synaptic weight strengths are governed by differential equations. Cells in the model are associated with neuroanatomical substrates and have been mapped to locations in Montreal Neurological Institute stereotactic space, providing a means to compare simulated and empirical fMRI data. The DIVA model also provides a computational and neurophysiological framework within which to interpret and organize research on speech acquisition and production in fluent and dysfluent child and adult speakers. The purpose of this review article is to demonstrate how the DIVA model is used to motivate and guide functional imaging studies. We describe how model predictions are evaluated using voxel-based, region-of-interest-based parametric analyses and inter-regional effective connectivity modeling of fMRI data.

A Neuroimaging Study of Premotor Lateralization and Cerebellar Involvement in the Production of Phonemes and Syllables

This study investigated the network of brain regions involved in overt production of vowels, monosyllables, and bisyllables to test hypotheses derived from the Directions Into Velocities of Articulators (DIVA) model of speech production (Guenther, Ghosh, & Tourville, 2006). The DIVA model predicts left lateralized activity in inferior frontal cortex when producing a single syllable or phoneme and increased cerebellar activity for consonant-vowel syllables compared with steady-state vowels. Sparse sampling functional magnetic resonance imaging (fMRI) was used to collect data from 10 right-handed speakers of American English while producing isolated monosyllables (e.g., "ba," "oo"). Data were analyzed using both voxel-based and participant-specific anatomical region-of-interest-based techniques. Overt production of single monosyllables activated a network of brain regions, including left ventral premotor cortex, left posterior inferior frontal gyrus, bilateral supplementary motor area, sensorimotor cortex, auditory cortex, thalamus, and cerebellum. Paravermal cerebellum showed greater activity for consonant-vowel syllables compared to vowels. The finding of left-lateralized premotor cortex activity supports the DIVA model prediction that this area contains cell populations representing syllable motor programs without regard for semantic content. Furthermore, the superior paravermal cerebellum is more active for consonant-vowel syllables compared with vowels, perhaps due to increased timing constraints for consonant production.