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Purpose: While the speech motor system is sensitive to feedback perturbations, sensory feedback does not seem to be critical to speech motor production. How the speech motor system is able to be so flexible in its use of sensory feedback remains an open question. Method: We draw on evidence from a variety of disciplines to summarize current understanding of the sensory systems' role in speech motor control, including both online control and motor learning. We focus particularly on computational models of speech motor control that incorporate sensory feedback, as these models provide clear encapsulations of different theories of sensory systems' function in speech production. These computational models include the well-established directions into velocities of articulators model and computational models that we have been developing in our labs based on the domain-general theory of state feedback control (feedback aware control of tasks in speech model). Results: After establishing the architecture of the models, we show that both the directions into velocities of articulators and state feedback control/feedback aware control of tasks models can replicate key behaviors related to sensory feedback in the speech motor system. Although the models agree on many points, the underlying architecture of the 2 models differs in a few key ways, leading to different predictions in certain areas. We cover key disagreements between the models to show the limits of our current understanding and point toward areas where future experimental studies can resolve these questions. Conclusions: Understanding the role of sensory information in the speech motor system is critical to understanding speech motor production and sensorimotor learning in healthy speakers as well as in disordered populations. Computational models, with their concrete implementations and testable predictions, are an important tool to understand this process. Comparison of different models can highlight areas of agreement and disagreement in the field and point toward future experiments to resolve important outstanding questions about the speech motor control system. [Paper presented at the International Conference on Speech Motor Control (7th).]

Neuroimaging has revealed a core network of cortical regions that contribute to speech production, but the functional organization of this network remains poorly understood. Purpose: We describe efforts to identify reliable boundaries around functionally homogenous regions within the cortical speech motor control network in order to improve the sensitivity of functional magnetic resonance imaging (fMRI) analyses of speech production and thus improve our understanding of the functional organization of speech production in the brain. Method: We used a bottom-up, data-driven approach by pooling data from 12 previously conducted fMRI studies of speech production involving the production of monosyllabic and bisyllabic words and pseudowords that ranged from single vowels and consonant-vowel pairs to short sentences (163 scanning sessions, 136 unique participants, 39 different speech conditions). After preprocessing all data through the same pipeline and registering individual contrast maps to a common surface space, hierarchical clustering was applied to contrast maps randomly sampled from the pooled data set in order to identify consistent functional boundaries across subjects and tasks. Boundary completion was achieved by applying adaptive smoothing and watershed segmentation to the thresholded population-level boundary map. Hierarchical clustering was applied to the mean within--functional region of interest (fROI) response to identify networks of fROIs that respond similarly during speech. Results: We identified highly reliable functional boundaries across the cortical areas involved in speech production. Boundary completion resulted in 117 fROIs in the left hemisphere and 109 in the right hemisphere. Clustering of the mean within-fROI response revealed a core sensorimotor network flanked by a speech motor planning network. The majority of the left inferior frontal gyrus clustered with the visual word form area and brain regions (e.g., anterior insula, dorsal anterior cingulate) associated with detecting salient sensory inputs and choosing the appropriate action. Conclusion: The fROIs provide insight into the organization of the speech production network and a valuable tool for studying speech production in the brain by improving within-group and between-groups comparisons of speech-related brain activity. [Paper presented at the International Conference on Speech Motor Control (7th).]

Speaking is one of the most complex motor behaviors developed to facilitate human communication. The underlying neural mechanisms of speech involve sensory–motor interactions that incorporate feedback information for online monitoring and control of produced speech sounds. In the present study, we adopted an auditory feedback pitch perturbation paradigm and combined it with functional magnetic resonance imaging (fMRI) recordings in order to identify brain areas involved in speech production and motor control. Subjects underwent fMRI scanning while they produced a steady vowel sound /a/ (speaking) or listened to the playback of their own vowel production (playback). During each condition, the auditory feedback from vowel production was either normal (no perturbation) or perturbed by an upward (+600 cents) pitch-shift stimulus randomly. Analysis of BOLD responses during speaking (with and without shift) vs. rest revealed activation of a complex network including bilateral superior temporal gyrus (STG), Heschl's gyrus, precentral gyrus, supplementary motor area (SMA), Rolandic operculum, postcentral gyrus and right inferior frontal gyrus (IFG). Performance correlation analysis showed that the subjects produced compensatory vocal responses that significantly correlated with BOLD response increases in bilateral STG and left precentral gyrus. However, during playback, the activation network was limited to cortical auditory areas including bilateral STG and Heschl's gyrus. Moreover, the contrast between speaking vs. playback highlighted a distinct functional network that included bilateral precentral gyrus, SMA, IFG, postcentral gyrus and insula. These findings suggest that speech motor control involves feedback error detection in sensory (e.g. auditory) cortices that subsequently activate motor-related areas for the adjustment of speech parameters during speaking. •Auditory feedback plays a key role in speech production and motor control.•Humans vocally compensate for pitch perturbations in their voice auditory feedback.•Vocal pitch motor control involves a complex sensory–motor network in the brain.•Functional networks of speech motor control are not affected in patients with epilepsy.

The relationship between speech production and perception is a topic of ongoing debate. Some argue that there is little interaction between the two, while others claim they share representations and processes. One perspective suggests increased recruitment of the speech motor system in demanding listening situations to facilitate perception. However, uncertainties persist regarding the specific regions involved and the listening conditions influencing its engagement. This study used activation likelihood estimation in coordinate‐based meta‐analyses to investigate the neural overlap between speech production and three speech perception conditions: speech‐in‐noise, spectrally degraded speech and linguistically complex speech. Neural overlap was observed in the left frontal, insular and temporal regions. Key nodes included the left frontal operculum (FOC), left posterior lateral part of the inferior frontal gyrus (IFG), left planum temporale (PT), and left pre‐supplementary motor area (pre‐SMA). The left IFG activation was consistently observed during linguistic processing, suggesting sensitivity to the linguistic content of speech. In comparison, the left pre‐SMA activation was observed when processing degraded and noisy signals, indicating sensitivity to signal quality. Activations of the left PT and FOC activation were noted in all conditions, with the posterior FOC area overlapping in all conditions. Our meta‐analysis reveals context‐independent (FOC, PT) and context‐dependent (pre‐SMA, posterior lateral IFG) regions within the speech motor system during challenging speech perception. These regions could contribute to sensorimotor integration and executive cognitive control for perception and production. This meta‐analysis revealed a common neural network between speech production and challenging speech perception tasks, supporting the role of the motor system in facilitating perception under difficult listening conditions. The extent of neural overlap varied depending on the listening conditions, with the left frontal operculum emerging as a hub for challenging speech perception.

Purpose: The clinical diagnosis of motor speech disorders (MSDs) is mainly based on perceptual approaches. However, studies on perceptual classification of MSDs often indicate low classification accuracy. The aim of this study was to determine in a forced-choice dichotomous decision-making task (a) how accuracy of speech-language pathologists (SLPs) in perceptually classifying apraxia of speech (AoS) and dysarthria is impacted by speech task, severity of MSD, and listener's expertise and (b) which perceptual features they use to classify. Method: Speech samples from 29 neurotypical speakers, 14 with hypokinetic dysarthria associated with Parkinson's disease (HD), 10 with poststroke AoS, and six with mixed dysarthria associated with amyotrophic lateral sclerosis (MD-FlSp [combining flaccid and spastic dysarthria]), were classified by 20 expert SLPs and 20 student SLPs. Speech samples were elicited in spontaneous speech, text reading, oral diadochokinetic (DDK) tasks, and a sample concatenating text reading and DDK. For each recorded speech sample, SLPs answered three dichotomic questions following a diagnostic approach, (a) neurotypical versus pathological speaker, (b) AoS versus dysarthria, and (c) MD-FlSp versus HD, and a multiple-choice question on the features their decision was based on. Results: Overall classification accuracy was 72% with good interrater reliability, varying with SLP expertise, speech task, and MSD severity. Correct classification of speech samples was higher for speakers with dysarthria than for AoS and higher for HD than for MD-FlSp. Samples elicited with continuous speech reached the best classification rates. An average number of three perceptual features were used for correct classifications, and their type and combination differed between the three MSDs. Conclusions: The auditory-perceptual classification of MSDs in a diagnostic approach reaches substantial performance only in expert SLPs with continuous speech samples, albeit with lower accuracy for AoS. Specific training associated with objective classification tools seems necessary to improve recognition of neurotypical speech and distinction between AoS and dysarthria.

Donald A. Robin University of Texas Health Science Center at San Antonio, and Honors College, University of Texas San Antonio Shannon N. Austermann Hula Skott E. Freedman San Diego State University and University of California, San Diego Gabriele Wulf University of Nevada, Las Vegas Kirrie J. Ballard University of Iowa, Iowa City, and University of Sydney, Lidcombe, Australia Richard A. Schmidt Emeritus, University of California, Los Angeles Contact author: Edwin Maas, Department of Speech, Language, and Hearing Sciences, University of Arizona, P.O. Box 210071, Tucson, AZ 85721-0071. E-mail: emaas{at}email.arizona.edu . Purpose: There has been renewed interest on the part of speech-language pathologists to understand how the motor system learns and determine whether principles of motor learning, derived from studies of nonspeech motor skills, apply to treatment of motor speech disorders. The purpose of this tutorial is to introduce principles that enhance motor learning for nonspeech motor skills and to examine the extent to which these principles apply in treatment of motor speech disorders. Method: This tutorial critically reviews various principles in the context of nonspeech motor learning by reviewing selected literature from the major journals in motor learning. The potential application of these principles to speech motor learning is then discussed by reviewing relevant literature on treatment of speech disorders. Specific attention is paid to how these principles may be incorporated into treatment for motor speech disorders. Conclusions: Evidence from nonspeech motor learning suggests that various principles may interact with each other and differentially affect diverse aspects of movements. Whereas few studies have directly examined these principles in speech motor (re)learning, available evidence suggests that these principles hold promise for treatment of motor speech disorders. Further research is necessary to determine which principles apply to speech motor (re)learning in impaired populations. Key Words: motor learning, motor speech disorders, conditions of practice, conditions of feedback CiteULike     Connotea     Del.icio.us     Digg     Facebook     Reddit     Technorati     Twitter     What's this?

Purpose: People who stutter (PWS) have more unstable speech motor systems than people who are typically fluent (PWTF). Here, we used real-time magnetic resonance imaging (MRI) of the vocal tract to assess variability and duration of movements of different articulators in PWS and PWTF during fluent speech production. Method: The vocal tracts of 28 adults with moderate to severe stuttering and 20 PWTF were scanned using MRI while repeating simple and complex pseudowords. Midsagittal images of the vocal tract from lips to larynx were reconstructed at 33.3 frames per second. For each participant, we measured the variability and duration of movements across multiple repetitions of the pseudowords in three selected articulators: the lips, tongue body, and velum. Results: PWS showed significantly greater speech movement variability than PWTF during fluent repetitions of pseudowords. The group difference was most evident for measurements of lip aperture using these stimuli, as reported previously, but here, we report that movements of the tongue body and velum were also affected during the same utterances. Variability was not affected by phonological complexity. Speech movement variability was unrelated to stuttering severity within the PWS group. PWS also showed longer speech movement durations relative to PWTF for fluent repetitions of multisyllabic pseudowords, and this group difference was even more evident as complexity increased. Conclusions: Using real-time MRI of the vocal tract, we found that PWS produced more variable movements than PWTF even during fluent productions of simple pseudowords. PWS also took longer to produce multisyllabic words relative to PWTF, particularly when words were more complex. This indicates general, trait-level differences in the control of the articulators between PWS and PWTF.

This study investigates the effects of executive functions and levodopa on articulatory timing patterns in simple and complex syllable onsets (CV vs. CCV) in Parkinson’s disease (PD). Kinematic speech data (EMA) of 25 individuals with PD in medication-OFF as well as medication-ON condition and 25 healthy controls (HC) were recorded, and group differences were examined. Results showed preserved articulatory coordination that is skewed in time in the PD group as well as a positive effect of levodopa on these patterns. Cluster analysis revealed an age-dependent decline in executive functions across groups that correlated with the shift pattern of the second consonant in CCV sequences for the PD group. This indicates that executive dysfunctions could give rise to changes in articulatory timing patterns as the disease progresses but independently of general motor severity.

Purpose: Despite having distinct etiologies, acquired apraxia of speech (AOS) and childhood apraxia of speech (CAS) share the same central diagnostic challenge (i.e., isolating markers specific to an impairment in speech motor planning/programming). The purpose of this review was to evaluate and compare the state of the evidence on approaches to differential diagnosis for AOS and CAS and to identify gaps in each literature that could provide directions for future research aimed to improve clinical diagnosis of these disorders. Method: We conducted a scoping review of literature published between 1997 and 2019, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. For both AOS and CAS, literature was charted and summarized around four main methodological approaches to diagnosis: speech symptoms, quantitative speech measures, impaired linguistic--motor processes, and neuroimaging. Results: Results showed that similar methodological approaches have been used to study differential diagnosis of apraxia of speech in adults and children; however, the specific measures that have received the most research attention differ between AOS and CAS. Several promising candidate markers for AOS and CAS have been identified; however, few studies report metrics that can be used to assess their diagnostic accuracy. Conclusions: Over the past two decades, there has been a proliferation of research identifying potential diagnostic markers of AOS and CAS. In order to improve clinical diagnosis of AOS and CAS, there is a need for studies testing the diagnostic accuracy of multiple candidate markers, better control over language impairment comorbidity, more inclusion of speech-disordered control groups, and an increased focus on translational work moving toward clinical implementation of promising measures.

Introduction: The current work presents a framework of motoric complexity where stimuli differ according to movement elements across a sound sequence (i.e., consonant transitions and vowel direction). This framework was then examined in children with childhood apraxia of speech (CAS), other speech sound disorders (SSDs), and typical development (TD). Method: Twenty-four children (CAS, n = 8; SSD, n = 8; TD, n = 8), 5-6 years of age, participated in this study. The children produced words that varied in motoric complexity while transcription, acoustic, and kinematic data were collected. Multidimensional analyses were conducted to examine speech production accuracy, speech motor variability, and temporal control. Results: Analyses revealed poorer accuracy, longer movement duration, and greater speech motor variability in children with CAS than TD (across all measures) and other SSDs (accuracy and variability). All children demonstrated greater speech motor variability and longer duration as movement demands increased within the framework of motoric complexity. Diagnostic grouping did not mediate performance on this task. Conclusions: Results of this study are believed to reveal gradations of complexity with increasing movement demands, thereby supporting the proposed framework of motoric complexity. This work also supports the importance of considering motoric properties of sound sequences when evaluating speech production skills and designing experimental and treatment stimuli.