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The DIVA model: A neural theory of speech acquisition and production
Open accessAbstract
The DIVA model of speech production provides a computationally and neuroanatomically explicit account of the network of brain regions involved in speech acquisition and production. An overview of the model is provided along with descriptions of the computations performed in the different brain regions represented in the model. The latest version of the model, which contains a new right-lateralised feedback control map in ventral premotor cortex, will be described, and experimental results that motivated this new model component will be discussed. Application of the model to the study and treatment of communication disorders will also be briefly described.
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“These results suggest that inner speech production is associated with a time-locked and content-specific internal forward model, similar to the one that operates in the production of overt speech.”
From Neurophysiological evidence of efference copies to inner speech (2017) · cites this paper as Tourville and Guenther, 2011
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“The lack of convergence in L-IFG is of interest as it is invariably a part of the corollary discharge network according to computational and neuroanatomical models of speech production (Chen et al., 2011; Tourville & Guenther, 2011).”
From Bridging phenomenology and neural mechanisms of inner speech: ALE meta-analysis on egocentricity and spontaneity in a dual-mechanistic framework (2023) · cites this paper as Tourville & Guenther, 2011
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Perturbing the consistency of auditory feedback in speech
Nault, D. R., Mitsuya, T., Purcell, D. W., & Munhall, K. G. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.905365 · Cited by 1
Task-Dependent Modulation of Auditory Feedback Control of Vocal Intensity
Hilger, A., Levant, S., Kim, J. H., Lester‐Smith, R. A., & Larson, C. R. (2022) · Journal of Voice · doi:10.1016/j.jvoice.2022.08.004 · Cited by 1
Qi, D., Kong, L., Yang, L., & Li, C. (2023) · International Symposium on Computer Engineering and Intelligent Communications (ISCEIC) · doi:10.1109/isceic59030.2023.10271237 · Cited by 1
Some properties of mental speech preparation as revealed by self-monitoring
Quené, H., & Nooteboom, S. G. (2024) · Speech Communication · doi:10.1016/j.specom.2024.103043 · Cited by 1
Audiomotor prediction errors drive speech adaptation even in the absence of overt movement
Parrell, B., Bae, M., Naber, C., Kim, O. A., Niziolek, C. A., & McDougle, S. D. (2026) · PLoS Biology · doi:10.1371/journal.pbio.3003862 · Cited by 1
“…in speech production, which suggests that adaptation is driven by the incorporation of corrective movements generated by a feedback controller in response to sensory errors into future feedforward motor programs [14, 13, 41], which itself is partially based on other ideas in limb control [42, 43].”
Timing of Speech in Brain and Glottis and the Feedback Delay Problem in Motor Control
Veillette, J. P., Rosen, J., Margoliash, D., & Nusbaum, H. C. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.2294-24.2025 · Cited by 1
Belyk, M. (2025) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780192894700.013.0025 · Cited by 1
Right-Hemispheric White Matter Organization Is Associated With Speech Timing in Autistic Children
Davison, K. E., Liu, T., Belisle, R. M., Perrachione, T. K., Qi, Z., Gabrieli, J. D. E., Tager-Flusberg, H. B., & Zuk, J. (2025) · Journal of Speech Language and Hearing Research · doi:10.1044/2025_jslhr-24-00548 · Cited by 1
“…speech and language areas including (a) the inferior frontal gyrus (IFG), a region known to subserve language production and speech motor control (Tourville & Guenther, 2011); (b) the superior temporal gyrus, a region known to play a major role in auditory processing of spectrotemporal…”
Feedback and feedforward control of intonation in cerebellar ataxia
Hilger, A., Cole, J., & Larson, C. (2025) · Language Cognition and Neuroscience · doi:10.1080/23273798.2025.2541696 · Cited by 1
“Speech production involves both segmental (e.g. phonemes, syllables) and suprasegmental (e.g. prosody, pitch, rhythm) mechanisms, each governed by overlapping but distinct control processes (Tourville & Guenther, 2011).”
InfluentialHöbler, F., Liu, Y., Beltz, A. M., Becker, H. C., Angstadt, M., Guenther, F. H., & Chang, S. (2025) · Neurobiology of Language · doi:10.1162/nol.a.26 · Cited by 1
“Within the DIVA framework, the right vPMC contains a lateralized feedback control map which receives error signals when incoming sensory feedback does not fall within the expected target region (Tourville & Guenther, 2011).”
InfluentialSelf-citationHolmes, M., Aalto, D., Patel, D., Ostevik, A. V., Jeffery, C. C., & Cummine, J. (2025) · Brain and Language · doi:10.1016/j.bandl.2025.105672 · Cited by 1
Kasper, J., Kell, C. A., & Perrier, P. (2025) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2025.101298 · Cited by 1
Sonj, S. S., Torabinezhad, F., Saffarian, A., Abolghasemi, J., & Behroozmand, R. (2025) · Journal of Voice · doi:10.1016/j.jvoice.2025.11.032 · Cited by 1
Speech Acoustic Markers Can Detect Mild Cognitive Impairment in Parkinson’s Disease
Smith, K. M., Williamson, J. R., & Quatieri, T. F. (2025) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2025.3620716 · Cited by 1
“Given that speech motor control also relies on basal ganglia pathways [42], the added cognitive demand of a speaking task in a talker with PD-MCI might thus lead to worsened compensatory resilience and further speech acoustic impairments.”
Effects of speech periodicity and speech rate on auditory-motor coupling during speech comprehension
Kwon, S., Lubinus, C., Kell, C. A., Keitel, A., & Rimmele, J. M. (2026) · Communications Biology · doi:10.1038/s42003-025-09481-y · Cited by 2
“A dorsal ( “ where ” or “ how ” ) stream connects the pSTG with Broca ’ s area 56 – 58 and other motor-related regions essential for auditory-motor integration 59 – 61 .”
The effect of transcranial direct current stimulation on reading adaptation in adults who stutter
Moein, N., Nguyen, C. M., Cheyne, D., & Nil, L. D. (2026) · Journal of Fluency Disorders · doi:10.1016/j.jfludis.2026.106195 · Cited by 1
Enhanced pitch centering in individuals with laryngeal dystonia
Rangwala, R., Subrahmanya, A., Brent, K., Gupta, S., Ranasinghe, K. G., Dale, C. L., Gaines, J. L., Pongos, A. L. A., Rosen, C. A., Schneider, S. L., et al. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1688947 · Cited by 1
Rivera, L. Z., Castro, C., Cuadros, J., Cortés, J. P., Weinstein, A., Espinoza, V. M., Prado, P., & Zañartu, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-49995-x · Cited by 3
Machine learning surrogate forward models for biomechanical laryngeal control
Parra, J. A., Sorolla, C., Quinteros, N. F., Ibarra, E., Alzamendi, G. A., Peterson, S. D., Weerathunge, H. R., Guenther, F. H., & Zañartu, M. (2026) · The Journal of the Acoustical Society of America · doi:10.1121/10.0044564 · Cited by 1
Self-citationTanaka, K., Shima, A., Omae, E., Toriyama, M., Miyake, T., Yamada, M., Cao, Y., Mima, T., & Koganemaru, S. (2026) · European Journal of Neuroscience · doi:10.1111/ejn.70688 · Cited by 1
Research on Cerebellar Contributions to Speech Acquisition and Production Based on DIVA Model
Wu, Y. Y., & Zhang, S. B. (2013) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.462-463.175 · Cited by 1
“In order to solve these problems, a DIVA model [5,6,7] that is closer to neuroanatomy is proposed by Guenther and Ghosh.”
MethodA Bidirectional Graphical Model for Babble-feedback Learning in Speech
Brady, M. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.11.106 · Cited by 1
“The Directions into Velocities of Articulators (DIVA) model of speech motor production (Tourville & Guenther, 2011) is in this camp of thinking and is a leading alternative to Articulatory Phonology.”
MethodRapp, B. C., & Miozzo, M. (2011) · Language and Cognitive Processes · doi:10.1080/01690965.2010.544595 · Cited by 1
“An illustration of this approach is provided by Guenther and Tourville (this issue 2011 ).”
MethodScheerer, N. E. (2016) · Scholars Commons (Wilfrid Laurier University) · Cited by 1
Cheng, X., Zhang, S., Yong, W. A. N. G., & LiWen, H. E. (2015) · Scientia Sinica Informationis · doi:10.1360/n112014-00187 · Cited by 1
Miyuki, Y., Hagiwara, Y., & Taniguchi, T. (2017) · Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics · doi:10.1109/devlrn.2017.8329801 · Cited by 1
“, learning during babbling [2]–[4].”
Vowel and Sibilant Production in Noise: Effects of Noise Frequency and Phonological Similarity
Reilly, K. J. (2020) · Journal of Speech Language and Hearing Research · doi:10.1044/2020_jslhr-19-00345 · Cited by 1
“While contemporary models of speech production account for a range of speech phenomena (Guenther, 2016; Houde & Nagarajan, 2011; Tourville & Guenther, 2011), they are not currently constructed to respond to factors that affect listeners’ speech comprehension.”
BackgroundThe Biolinguistic Instantiation: Form to Meaning in Brain/Syllable Interactions
Bakrim, N. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.89943 · Cited by 1
Tilsen, S. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.801740 · Cited by 1
“This construct is justifiable on the grounds that spectral characteristics 277 of waveforms associated with vowels are determined primarily by the geometry of the vocal tract (32), and 278 current models of speech motor control agree that parameters must exist which define targets for the 279 articulators which determ…”
BackgroundKröger, B. J. (2017) · Springer eBooks · doi:10.1007/978-3-662-55459-3_4 · Cited by 1
Event-Related Potential Correlates of Learning to Produce Novel Foreign Phonemes
Railo, H., Varjonen, A., Lehtonen, M., & Sikka, P. (2022) · Neurobiology of Language · doi:10.1162/nol_a_00080 · Cited by 1
Bilingual lips: Motor activation for second language word processing
Barragán, B., Alvarado, E., Cordon, S., Samols, J., & Zandona, M. (2023) · International Journal of Bilingualism · doi:10.1177/13670069231156535 · Cited by 1
“This comparison may improve future productions, as stronger motor programs would support speech perception through a top-down mechanism that helps disambiguate phonological information (Du et al., 2014; Sato et al., 2009; Tourville & Guenther, 2011).”
BackgroundChaldi, D. (2023) · Review of European Studies · doi:10.5539/res.v15n1p42 · Cited by 1
“…of this intervention is that it is easily manageable from clinicians, compared to other complex treatment packages such as the traditional articulation therapy and the Directions into Velocities of Articulators (DIVA), which provides a monitoring feedback as you speak (Tourville & Guenther, 2011).”
Deep Reinforcement Learning for Articulatory Synthesis in a Vowel-to-Vowel Imitation Task
Дмитриевич, Ш. Д., Pirogova, E., Wysocki, T. A., & Lech, M. (2023) · Sensors · doi:10.3390/s23073437 · Cited by 1
“Authors in [25] encoded auditory targets for their model of speech production using three pairs of inputs describing the upper and lower bounds for the first, second, and third formant frequencies of the speech sound.”
Alves, M. D. C., Mancini, P. C., & Teixeira, L. C. (2023) · CoDAS · doi:10.1590/2317-1782/20232022202pt · Cited by 1
Alves, M. D. C., Mancini, P. C., & Teixeira, L. C. (2024) · Journal of Voice · doi:10.1016/j.jvoice.2023.10.025 · Cited by 1
Тhe role of auditory feedback in voice control with normal and impaired hearing
Lunichkin, А. М., & Shtin, K. S. (2023) · Сенсорные системы · doi:10.31857/s0235009223040042 · Cited by 1
The Role of Auditory Feedback in Voice Control in Normal and Impaired Hearing
Lunichkin, А. М., & Shtin, K. S. (2024) · Neuroscience and Behavioral Physiology · doi:10.1007/s11055-024-01616-8 · Cited by 1
“This type of control is used by vocalists who perform with orchestras, in conditions of polyphony, and in choral singing [Tourville and Guenther, 2011; Selleck and Sataloff, 2014].”
Alqhazo, M. T., Blomgren, M., Baroudi, A., Alkhamaiseh, Z., & Al-Hiari, A. A. (2024) · Hearing Balance and Communication · doi:10.4103/hbc.hbc_29_24 · Cited by 1
Revisiting the boundaries of different altered accents profiles
Berthier, M. L., Moreno‐Torres, I., Verhoeven, J., & Dávila, G. (2025) · Cortex · doi:10.1016/j.cortex.2025.01.001 · Cited by 1
Neural correlates of foreign speech imitation: The effects of age and music
Yan, X., Mao, J., Ma, Z., Perkins, K., Li, W., Wang, Y., & Cao, F. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.75 · Cited by 1
“The Directions into Velocities of Articulators (DIVA) model provides a framework for understanding the neural mechanisms underlying foreign speech imitation ( Tourville & Guenther, 2011), which is essentially a process of adjusting motor control according to somato-sensory and auditory feedback.”
InfluentialEfficient Bootstrapping of Vocalization Skills Using Active Goal Babbling
Philippsen, A. K., Reinhart, F., & Wrede, B. (2015) · Publikationen an der Universität Bielefeld (Universität Bielefeld) · Cited by 1
Research on the fMRI study of Chinese speech region of interest based on DIVA model
Zhang, S., Xu, J. B., & Liu, Y. (2016) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2016.7531958 · Cited by 1
Hyperarticulation aids learning of new vowels in a developmental speech acquisition model
Philippsen, A. K., Reinhart, F., Wrede, B., & Wagner, P. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7965833 · Cited by 1
“Almost all computational models of speech acquisition incorporate a babbling phase where motor configurations are explored randomly [1], [2], [3] or actively [4], [5], [6].”
BackgroundCOSMO SylPhon: A Bayesian Perceptuo-motor Model to Assess Phonological Learning
Barnaud, M.-L., Diard, J., Bessìère, P., & Schwartz, J. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-73 · Cited by 1
“Some global models study speech development as a whole [4, 5], while other models focus on the understanding of some aspects of phonological development [6, 7, 8, 9].”
BackgroundAssessing Acoustic and Articulatory Dimensions of Speech Motor Adaptation with Random Forests
Klein, E., Brunner, J., & Hoole, P. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1812 · Cited by 1
“These findings are supported by more recent neuroimaging results which suggest that sensory representations of speech sounds are defined in both auditory and somatosensory cortex and are characterized by neural auditory-somatosensory mappings [3, 4].”
ResultInvestigation of a Method for EEG Signal De-Noising Based on the DIVA Model
Zhang, S., Jiao, L., & Zhou, N. (2020) · Frontiers in artificial intelligence and applications · doi:10.3233/faia200686 · Cited by 1
“The principle is shown in Figure 2[3].”
Predictive articulatory speech synthesis with semantic discrimination
Schmidt-Barbo, P., Shafaei-Bajestan, E., & Sering, K. F. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4656098 · Cited by 1
Response patterns to vowel formant perturbations in children
Cheung, S. T., Thompson, K., Chen, J. L., Yunusova, Y., & Beal, D. S. (2021) · The Journal of the Acoustical Society of America · doi:10.1121/10.0006567 · Cited by 1
The effects of high-frequency hearing loss on speech perception in people with aphasia
Gaven, J. M. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/7py6-yq62 · Cited by 1
Consistency of children’s hemodynamic responses during spontaneous speech
Tichenor, S. E., Walsh, B. M., Gerwin, K. L., & Tian, F. (2022) · Neurophotonics · doi:10.1117/1.nph.9.1.015003 · Cited by 1
“This planned word form is translated to the speech-motor system, which sends signals to the articulators for production.(8,9) Investigations into the neurophysiology of speech production and language formulation over the past few decades have led to a deeper understanding of how language formulation and speech product…”
BackgroundInfluentialPlum, L., van der Meulen, I., Krzok, F., Overbeck, R., van de Sandt‐Koenderman, W. M. E., Willmes, K., Binkofski, F., & Bruehl, S. (2022) · Child Language Teaching and Therapy · doi:10.1177/02656590221111346 · Cited by 1
“…are various explanations, theories and models of speech and language acquisition, development and production (cf. Chomsky, 2005; Schoneberger, 2010; Tourville & Guenther, 2011), there is no model conception from the field of language development that could support MCM yet because no model…”
BackgroundOutgrowing Speech Sound Disorders
Westby, C. E. (2022) · Word of Mouth · doi:10.1177/10483950221101453a · Cited by 1
Speech Production, Neural Basis of
Baldo, J. V., & Dronkers, N. F. (2015) · Elsevier eBooks · doi:10.1016/b978-0-08-097086-8.54018-5 · Cited by 1
Current Models of Speech Motor Control: A Control-Theoretic Overview of Architectures & PropertiesPreprint
Parrell, B., Lammert, A. C., Ciccarelli, G. A., & Quatieri, T. F. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/197285 · Cited by 1
“DIVA In the context of classical control description, DIVA is a hybrid system combining a model-predictive controller with separate auditory and somatosensory feedback controller loops (Guenther et al., 2006; Tourville & Guenther, 2011; Golfinopoulos et al., 2011).”
MethodRollans, C. (2016) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3n58cx05 · Cited by 1
“The DIVA model also describes a feedback loop, which helps track and correct speech production errors and aids in speech development (Guenther, 2006; Tourville & Guenther, 2011).”
BackgroundNeuromotor Organization of Speech Production
Tremblay, P., Deschamps, I., & Dick, A. S. (2019) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190672027.013.15 · Cited by 3
Expectancy changes the self-monitoring of voice identityPreprint
Johnson, J. F., Belyk, M., Schwartze, M., Pinheiro, A. P., & Kotz, S. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.22.215350 · Cited by 1
Cortical network underlying speech production during delayed auditory feedbackPreprint
Özker, M., Doyle, W., Devinsky, O., & Flinker, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.11.378471 · Cited by 1
“This auditory error signal is then relayed to vocal-motor regions for the real-time correction of vocalization in order to produce the intended speech [8-10].”
BackgroundRodd, J. M. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.1 · Cited by 2
The Structure of The Lexical Item and Sentence Meaning Composition
Piñango, M. M. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.13 · Cited by 1
Systematicity and Arbitrariness In Language
Yang, C. D. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.9 · Cited by 1
Neural encoding of speech and word forms
Poeppel, D., & Sun, Y. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.16 · Cited by 1
Disorders of Lexical Access And Production
Mirman, D., & Middleton, E. L. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.28 · Cited by 1
Differences in vocabulary growth across groups and individuals
Potter, C. E., & Lew‐Williams, C. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.22 · Cited by 1
Lexical representation and access in sign languages
Mayberry, R. I., & Wille, B. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.27 · Cited by 1
Cortical dynamics of speech feedback control in non-fluent Primary Progressive AphasiaPreprint
Kothare, H., Ranasinghe, K. G., Hinkley, L. B. N., Mizuiri, D., Licata, A. E., Lauricella, M., Honma, S. M., Borghesani, V., Dale, C. L., Shwe, W., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.28.501928 · Cited by 1
Neural representation of phonological wordform in bilateral posterior temporal cortexPreprint
Sorensen, D. O., Avcu, E., Lynch, S., Ahlfors, S. P., & Gow, D. W. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.19.549751 · Cited by 1
“The relationship between the somatosensory cortex and supramarginal gyrus relies on articulatory positions, supported by arguments that somatosensory target and state maps reside in the SMG (Hickok & Poeppel, 2000; Tourville & Guenther, 2011).”
Bridging verbal coordination and neural dynamicsPreprint
Schwab-Mohamed, I., Mercier, M. R., Trébuchon, A., Morillon, B., Lancia, L., & Schön, D. (2024) · eLife · doi:10.7554/elife.99547.1 · Cited by 1
Lu, Y., Tang, X., Xiao, Z., Xu, A., Chen, J., & Tian, X. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.27.620480 · Cited by 1
The implementation methods of Chinese phonetic brain-machine interface based on DIVA model
Zhang, S., & You, Z. (2015) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2015.7161874
A cerebellar neuronal network model applying to DIVA model
Wu, Y., & Shaobai, Z. (2014) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2014.6852562
DIVA model based on the constant delay mechanism
Song, F., & Shaobai, Z. (2014) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2014.6852998
Improvement and discussion on pronunciation method of DIVA model based on auditory perception space
Zhang, S., & Xu, X.-B. (2016) · International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) · doi:10.1109/dipdmwc.2016.7529354
Mapping acoustics to kinematics in speech
Bali, R. (2016) · TSpace (University of Toronto)
Etter, N. M. (2014) · UKnowledge (University of Kentucky)
A DIVA-based method for the phonation of the Chinese Diphthongs
Zhang, S., Zhang, T., & Ningning, Z. (2017) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2017.7979331
Unsupervised learning of vocal tract sensory-motor synergies
Wagner, W. J. (2017) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)
Resting State Networks in Individuals with and without Reading Disorders
Grandmont, D. M. (2018) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r31r6ng05
“…auditory errors) and a somatosensory subsystem (with connections between the somatosensory cortex and the premotor and motor cortices to encode speech sound targets and resolve somatosensory errors) (Guenther, 2006; Guenther et al., 2006; Guenther & Vladusich, 2012; Tourville & Guenther, 2011).”
BackgroundMethodInfluentialJenson, D. E. (2018) · doi:10.21007/etd.cghs.2018.0449 · Cited by 1
“Conversely, inverse internal models constitute a mapping from the sensory domain to the motor domain and have been considered a mechanism for implementing corrective motor commands based on detected (Tourville and Guenther, 2011; Guenther and Vladusich, 2012) or predicted (Houde and Nagarajan, 2011; Houde and Chang, 2…”
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An fMRI study of initiation and inhibition of manual and spoken responses in people who stutterPreprint
Wiltshire, C. E. E., & Watkins, K. E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.17605/osf.io/zjswa
Gattie, M., Lieven, E., & Kluk, K. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.24.396283
Investigating Perceptual Subgroups of Ataxic Dysarthria
Gore, M. (2020) · ResearchWorks at the University of Washington (University of Washington)
Chew on this! Oral stereognosis predicts visual word recognition in typical adults
Cummine, J., Huynh, T. K. T., Cullum, A., Ostevik, A. V., & Hodgetts, W. E. (2021) · Current Psychology · doi:10.1007/s12144-021-01647-1
A Critical Review of Apraxia of Speech and Childhood Apraxia of Speech
Smith, M. J. (2021) · University of New Hampshire Scholars Repository (University of New Hampshire at Manchester)
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Enhancing Discrimination and Production of English Vowels Using Task-Based Training
Ebedy, H. G. M. (2021) · مجلة کلية التربية · doi:10.21608/jfe.2021.192456
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Learning and Using Written Word Forms
Rebecca Treiman, A., & Kessler, B. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.24
Learning words amidst speech sound variability
Creel, S. C. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.7
The Dynamics of Word Production
Kilbourn-Ceron, O., & Goldrick, M. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.25
How Learners Move From Sound To Morphology
Demuth, K. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.8
Crain, S. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.11
Metadata
- Authors
- Tourville, J. A., & Guenther, F. H.
- Affiliations
- Boston University (US)
- Corresponding author
- Guenther, Frank H.
- Venue
- Language and Cognitive Processes
- Publisher
- Informa UK Limited
- Published
- 2011-08
- Volume
- 26
- Issue
- 7
- Pages
- 952-981
- ISSN
- 0169-0965 (ISSN-L)
- 1464-0732
- PMC
- PMC3650855
- OpenAlex
- W2011934537
- Cited by
- 748
- References
- 178
- Citations by year
- 2026: 67 · 2025: 77 · 2024: 68 · 2023: 65 · 2022: 112 · 2021: 58 · 2020: 57 · 2019: 60 · 2018: 45 · 2017: 38 · 2016: 27 · 2015: 28 · 2014: 22 · 2013: 15 · 2012: 5
- FWCI
- 4.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Language and Cognitive Processes (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Deafness and Other Communication Disorders (R01DC002852)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Phonetics and Phonology Research
- Keywords
- DIVA model, speech production, speech development, ventral premotor cortex, right lateralization, feedback control, brain regions, computational model, communication disorders
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