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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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Items 401–500 of 747
Brain-to-speech decoding will require linguistic and pragmatic data
Li, L., & Negoita, S. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aae466 · Cited by 4
“Researchers have made tremendous progress in characterizing the neural substrates of the motor execution of speech (Tourville and Guenther 2011) and the neural language network more broadly (Friederici 2002, Hagoort 2005, Xiang et al 2010, Catani and Bambini 2014, Fedorenko and Thompson-Schill…”
BackgroundContributions of pragmatics to word learning and interpretation
Grigoroglou, M., & Anna Papafragou, A. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.21 · Cited by 6
Yeatman, J. D. (2022) · Wiley eBooks · doi:10.1002/9781119705116.ch24 · Cited by 4
Belyk, M., & Kotz, S. A. (2024) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2024.101243 · Cited by 4
Consonant and vowel articulation accuracy in younger and middle-aged Spanish healthy adults
Moreno‐Torres, I., & Baro, E. N. (2020) · PLoS ONE · doi:10.1371/journal.pone.0242018 · Cited by 4
Auditory-Motor Function Pre- and Post-Therapy in Hyperfunctional Voice Disorders: A Case Series
Abur, D., Hillman, R. E., & Stepp, C. E. (2023) · Journal of Voice · doi:10.1016/j.jvoice.2023.08.017 · Cited by 4
“32 Therefore, the persistent improper laryngeal control in individuals with HVDs may result from atypical auditory-motor function.”
Syllable as a Synchronization Mechanism That Makes Human Speech Possible
Xu, Y. (2024) · Brain Sciences · doi:10.3390/brainsci15010033 · Cited by 23
“…approximation , as discussed in Section 3 (cf., in particular, the graphic illustra‑ tion in Figures 4 and 6), implies that the neural commands sent to the articulatory mus‑ cles are in the form of underlying targets rather than either surface displacement or veloc‑ ity [238,239] trajectories.”
A Biopsychosocial Overview of Speech Disorders: Neuroanatomical, Genetic, and Environmental Insights
Jaishankar, D., Raghuram, T., Raju, B. K., Swarna, D., Parekh, S., Chirmule, N. B., & Gujar, V. (2025) · Biomedicines · doi:10.3390/biomedicines13010239 · Cited by 4
Sarmukadam, K., Nejati, V., & Behroozmand, R. (2025) · Cortex · doi:10.1016/j.cortex.2025.06.008 · Cited by 5
Wu, X., Zhang, B., Wei, L., Liu, H., Liu, P., & Wang, W. (2020) · Neuroreport · doi:10.1097/wnr.0000000000001410 · Cited by 4
Will Brain Imaging Lead to a Translational Neuroscience of Stuttering?
Bowers, A. L. (2023) · Perspectives of the ASHA Special Interest Groups · doi:10.1044/2023_persp-23-00007 · Cited by 5
Southby, L. (2024) · International Journal of Language & Communication Disorders · doi:10.1111/1460-6984.12993 · Cited by 4
Endogenous auditory and motor brain rhythms predict individual speech tracking
Lubinus, C., Keitel, A., Obleser, J., Poeppel, D., & Rimmele, J. M. (2026) · PLoS Biology · doi:10.1371/journal.pbio.3003924 · Cited by 7
Richardson, J. D., & Fridriksson, J. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-407794-2.00010-9 · Cited by 4
Deriving the onset and offset times of planning units from acoustic and articulatory measurements
Rodd, J., Bosker, H. R., ten Bosch, L., & Ernestus, M. (2019) · The Journal of the Acoustical Society of America · doi:10.1121/1.5089456 · Cited by 4
“The DIVA model (Tourville and Guenther, 2011) operationalizes the planning units by defining them in terms of upper and lower bounds for articulator positions, and upper and lower bounds for expected fundamental frequency and formants.”
No Evidence for Dystonia-Like Sensory Overflow of Tongue Representations in Adults Who Stutter
Vreeswijk, S. M. E., Hoang, T. N. L., Korzeczek, A., Neef, N. E., von Gudenberg, A. W., Paulus, W., & Sommer, M. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00336 · Cited by 4
Masking auditory feedback does not eliminate repetition reduction
Jacobs, C. L., Loucks, T. M. J., Watson, D. G., & Dell, G. S. (2019) · Language Cognition and Neuroscience · doi:10.1080/23273798.2019.1693051 · Cited by 4
Spencer, K. A., Amaral, J., & Lansford, K. L. (2022) · American Journal of Speech-Language Pathology · doi:10.1044/2022_ajslp-22-00159 · Cited by 4
“Because speech is rapid, it is not as easily informed by the feedback control loop, using the error of one movement to inform the next (Tourville & Guenther, 2011).”
BackgroundTomassi, N. E., Weerathunge, H. R., Cushman, M. R., Bohland, J. W., & Stepp, C. E. (2021) · Journal of Speech Language and Hearing Research · doi:10.1044/2021_jslhr-21-00377 · Cited by 4
“Previously learned programs are used to send motor commands to speech articulators, whereas feedback systems are responsible for detecting and correcting errors between expected and perceived sensory signals (Houde & Nagarajan, 2011; Parrell et al., 2019; Tourville & Guenther, 2011).”
BackgroundInfluentialBeguš, G., & Zhou, A. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-11219 · Cited by 4
“Earlier neural models replicate brain mechanisms behind perception and production [9], but they do not focus on lexical learning or classification and do not include recent progress in performance of deep learning architectures.”
BackgroundNing, L.-H. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1058080 · Cited by 6
“Neurocomputational models of speech-motor control such as the Directions into Velocities of Articulators (Guenther, 1995; Guenther et al., 1998; Tourville and Guenther, 2011) and the State Feedback Control (Houde and Nagarajan, 2011; Houde et al.”
BackgroundFeedback Schedule Effects on Speech Motor Learning in Older Adults
Weir-Mayta, P., Hamilton, A., Stockton, J. D., & Munoz, C. (2022) · Physical Activity and Health · doi:10.5334/paah.205 · Cited by 3
Kinahan, S. P., Liss, J., & Berisha, V. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281306 · Cited by 4
“The inverse-map elements are static during normal DIVA operation, however they are trained during a babbling phase of random speech articulator movements [6].”
BackgroundMethodInfluentialHow Sensorimotor Interactions Enable Sentence Imitation
Hung, T.-W. (2015) · Minds and Machines · doi:10.1007/s11023-015-9384-8 · Cited by 4
Etter, N. M., McKeon, P. O., Dressler, E. V. M., & Andreatta, R. D. (2017) · International Journal of Speech-Language Pathology · doi:10.1080/17549507.2017.1318303 · Cited by 4
“…10.1080/17549507.2017.1318303 of auditory and somatosensory error maps based upon real-time self-generated and performancerelated afference (Bohland, Bullock, & Guenther, 2010; Golfinopoulos, Tourville, & Guenther, 2010; Guenther & Vladusich, 2012; Terband et al., 2014; Tourville & Guenther, 2011).”
BackgroundInfluentialMénard, L., Perrier, P., & Aubin, J. (2016) · The Journal of the Acoustical Society of America · doi:10.1121/1.4945718 · Cited by 9
A mixed generative model of auditory word repetitionPreprint
Sajid, N., Holmes, E., Costa, L. D., Price, C. J., & Friston, K. J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.20.477138 · Cited by 4
“Adaptive feedforward neural network (Guenther, 1994; Tourville and Guenther, 2011) The neural network (DIVA; Directions Into Velocities of Articulators) was trained in two steps for learning the phonetic-to-orosensory and orosensory-to-articulatory mappings.”
Infants’ Learning of Speech Sounds and Word-Forms
Swingley, D. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.6 · Cited by 4
Landau, B. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.5 · Cited by 4
An fMRI study of initiation and inhibition of manual responses in people who stutterPreprint
Wiltshire, C. E. E., Chesters, J., Krishnan, S., Healy, M. P., & Watkins, K. E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.07.286013 · Cited by 4
Semantic network activation facilitates oral word reading in chronic aphasia
Pillay, S. B., Gross, W. L., Heffernan, J., Book, D., & Binder, J. (2022) · Brain and Language · doi:10.1016/j.bandl.2022.105164 · Cited by 4
A Matter of Time: A Web-Based Investigation of Rest and Sleep Effects on Speech Motor Learning
van Zelst, A. L., & Earle, F. S. (2023) · Journal of Speech Language and Hearing Research · doi:10.1044/2023_jslhr-22-00309 · Cited by 3
Improving Stuttering Through Augmented Multisensory Feedback Stimulation
Muscarà, G., Vergallito, A., Letorio, V., Iannaccone, G., Giardini, M., Randaccio, E., Scaramuzza, C., Russo, C., Scarale, M. G., & Abutalebi, J. (2025) · Brain Sciences · doi:10.3390/brainsci15030246 · Cited by 3
“In the current work, we focus on one of these models, namely the Directions into Velocities of Articulators (DIVA) [28,29] since it has often been applied to stuttering [30,31] and is deeply related to our intervention.”
An fMRI study of initiation and inhibition of manual and spoken responses in people who stutter
Wiltshire, C. E. E., Chesters, J., Krishnan, S., Cler, G. J., Healy, M. P., & Watkins, K. E. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.89 · Cited by 3
“Alterna-tively, the DIVA model predicts that the right IFC is sensitive to error detection ( Tourville & Guenther, 2010), and therefore might be more active in people who stutter during the “unsuccessful stop” stop trials.”
Hanford, L. C., Jacoby, J. R., Salat, D. H., Arnold, S. E., & Eshghi, M. (2025) · Human Brain Mapping · doi:10.1002/hbm.70288 · Cited by 3
“Speech regions- of-interest (ROIs) were selected for their neural representation of speech motor control, including planning, sequencing, and execution of speech behaviors (Tourville and Guenther 2011).”
Embracing variability: toward proactive and precision-based voice science
Hunter, E. J., & Berardi, M. L. (2025) · Logopedics Phoniatrics Vocology · doi:10.1080/14015439.2025.2562929 · Cited by 3
“…across articulatory ranges [37]; ecological accounts demonstrate lawful adaptation of vocal output to ambient noise and communicative context [54,55]; and motor-control models (e.g. state-feedback) predict corrective adjustments proportional to prediction error and task constraints [42,43].”
Dmitrieva, X., Anton, J., Fairs, A., Ivanova, B., Sein, J., Nazarian, B., Dufour, S., Friedemann Pulvermüller, A., Runnqvist, E., & Strijkers, K. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf275 · Cited by 4
“…(Indefrey and Levelt 2004; Hickok and Poeppel 2007; Indefrey 2011; Hickok 2012; de Zubicaray 2023), or whether it is fully integrated across the language modalities (Pulvermüller 1999, 2018; Pulvermüller and Fadiga 2010; Tourville and Guenther 2011; Strijkers and Costa 2016; Kerr et al. 2023).”
Dyslin, S. M., DeMarco, A. T., Staples, R., Dickens, J. V., Snider, S. F., Friedman, R. B., & Turkeltaub, P. E. (2025) · Neurobiology of Language · doi:10.1162/nol.a.216 · Cited by 3
Sensory Feedback in Parkinson Disease Voice Production: A Systematic Review
Contreras-Ruston, F., Duggirala, S. X., Wingbermühle, J., Navarra, J., & Kotz, S. A. (2025) · Journal of Voice · doi:10.1016/j.jvoice.2025.02.039 · Cited by 4
An active inference account of stuttering behavior
Usler, E. R. (2025) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1498423 · Cited by 5
“…kinematics and inverse modeling (Parrell et al., 2019), including two models that have influenced current perspectives on stuttering: the Directions Into Velocities of Articulators (DIVA) (Tourville and Guenther, 2011) and the Hierarchical State Feedback Control (HSFC) models (Hickok et al., 2011).”
InfluentialSusceptibility to auditory feedback manipulations and individual variability
Özker, M., & Peter Hagoort, A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0323201 · Cited by 3
“If there is a mismatch between the prediction and the auditory feedback of the produced speech, e.g., when auditory feedback is perturbed, an error signal will be generated to adjust the motor command and correct the speech error [41–43].”
Jenson, D. (2021) · PLoS ONE · doi:10.1371/journal.pone.0258335 · Cited by 4
Understanding how the human brain tracks emitted speech sounds to execute fluent speech production
Callan, A. M., & Callan, D. E. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001533 · Cited by 3
“(C) A cortical model of speech production based on the DIVA model and Ozker’s study (adapted from Guenther and Vladusich [8]).”
MethodClinically Measuring Orofacial Somatosensation in a Cohort of Healthy Aging Adults
Etter, N. M., Schmauk, N., & Neely, K. A. (2022) · American Journal of Speech-Language Pathology · doi:10.1044/2022_ajslp-22-00078 · Cited by 3
Singing and Speaking Ability in Parkinson's Disease and Spinocerebellar Ataxia
Kim, Y., Sidtis, D. V. L., & Sidtis, J. J. (2023) · Journal of Speech Language and Hearing Research · doi:10.1044/2022_jslhr-22-00274 · Cited by 4
“As an alternative explanation has been advanced that externally cued tasks require less cognitive–linguistic demands than spontaneous speech (Bohland et al., 2010; Feenaughty et al., 2013; Huber & Darling, 2011; Tourville & Guenther, 2011; Weir-Mayta et al., 2017).”
BackgroundAaron, A. S., Abur, D., Volk, K. P., Noordzij, J. P., Tracy, L. F., & Stepp, C. E. (2023) · Journal of Voice · doi:10.1016/j.jvoice.2023.01.008 · Cited by 3
“Models of speech motor control suggest that targets of speech production are auditory and that detection of auditory errors is crucial for integration of auditory feedback into feedforward commands.(1) The Directions Into Velocities of Articulators (DIVA) model of speech production posits that speakers who can better…”
BackgroundMethodMuñoz, L. E., Kartushina, N., & Mayor, J. (2024) · Developmental Science · doi:10.1111/desc.13477 · Cited by 4
Semantic Representations of Speech Production in Intracranial EEG
Herff, C., Verwoert, M., Amigó-Vega, J., & Ottenhoff, M. C. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394550 · Cited by 3
“There is a clear accumulation of significant channels around the central sulcus and in superior temporal areas, which are known to be involved in speech production [46], [47].”
BackgroundEnhancing speech perception in noise through articulation
Perron, M., Liu, Q., Tremblay, P., & Alain, C. (2024) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.15179 · Cited by 5
“According to the DIVA (Directions Into Velocities of Articulators) model of speech production, 35,36 the left ventral premotor cortex plays an essential role in housing speech motor representations.”
Cortical Tracking of Speech Is Reduced in Adults Who Stutter When Listening for Speaking
Gastaldon, S., Busan, P., Molinaro, N., & Lizarazu, M. (2024) · Journal of Speech Language and Hearing Research · doi:10.1044/2024_jslhr-24-00227 · Cited by 4
Brown, E. C., Bowers, A. L., Rafferty, M. B., Casenhiser, D. M., Reilly, K. J., Harkrider, A. W., & Saltuklaroglu, T. (2024) · Clinical Neurophysiology · doi:10.1016/j.clinph.2024.10.017 · Cited by 4
Johnson, R., Matthews, J., Diawara, N., & Carroll, R. M. (2020) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2020.00032 · Cited by 3
Influence of Altered Auditory Feedback on Oral–Nasal Balance in Speakers of Brazilian Portuguese
de Boer, G., Marino, V. C. D. C., Berti, L. C., Fabron, E. M. G., Spazzapan, E. A., & Bressmann, T. (2019) · Journal of Speech Language and Hearing Research · doi:10.1044/2019_jslhr-s-18-0051 · Cited by 3
Effects of phonological features on reading-aloud latencies: A cross-linguistic comparison.
Ulicheva, A., Roon, K. D., Cherkasova, Z., & Mousikou, P. (2021) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/xlm0000893 · Cited by 3
Cai, X., Yin, Y., & Zhang, Q. (2023) · Bilingualism Language and Cognition · doi:10.1017/s1366728923000019 · Cited by 4
Effects of Language on Motor Processes in Development
Goffman, L. (2015) · Wiley eBooks · doi:10.1002/9781118584156.ch24 · Cited by 3
Adaptation to temporal structure
Schwartze, M. (2012) · Max Planck Digital Library · Cited by 3
Sensorimotor experience in speech perception
Schuerman, W. L. (2017) · Radboud Repository (Radboud University) · Cited by 3
Differential causal involvement of human auditory and frontal cortices in vocal motor controlPreprint
Cardenas, A. R., Behroozmand, R., Kocsis, Z., Gander, P. E., Nourski, K. V., Kovach, C. K., Ibayashi, K., Pipoly, M., Kawasaki, H., Howard, M. A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.08.139881 · Cited by 2
Phonological variation and lexical form
Purse, R., Tamminga, M., & White, Y. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.15 · Cited by 3
Schwarz, F., & Zehr, J. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.18 · Cited by 3
Efficient Communication and The Organization of The Lexicon
Mahowald, K., Dautriche, I., Braginsky, M., & Gibson, T. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.19 · Cited by 7
Auditory-motor synchronization determines the use of predictions in music perceptionPreprint
Hartung, N., Barchet, A. V., Abel, C., Pittman-Polletta, B., Ullén, F., & Rimmele, J. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.20.671291 · Cited by 4
Johari, K., & Tabari, F. (2024) · Brain Research · doi:10.1016/j.brainres.2024.149434 · Cited by 2
Brain Dynamics of Speech Modes Encoding: Loud and Whispered Speech Versus Standard Speech
Sanders, B., Lancheros, M., Bourqui, M., & Laganaro, M. (2025) · Brain Topography · doi:10.1007/s10548-025-01108-z · Cited by 3
“Indeed, although there is no input so far on the dynamics of brain activation in the latter (Tourville & Guenther 2011), the present findings challenge our comprehension of the feedforward control system.”
An Emergentist Account of Language in the Brain—Seeking Neural Synergies Behind Human Uniqueness
Bourguignon, N. J., & Bue, S. L. (2025) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02331 · Cited by 4
Zhang, Y., Sarmukadam, K., Nejati, V., & Behroozmand, R. (2025) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2025.109202 · Cited by 3
“According to established sensorimotor integration models (Guenther, 2016; Tourville and Guenther, 2011), the speech system strives to correct only those errors deemed internally relevant while downweighing perceived external perturbations that do not reflect true self-generated deviations.”
InfluentialRowe, H. P., Miller, H. E., Belisle, R. M., Perrachione, T. K., & Guenther, F. H. (2025) · Neurobiology of Language · doi:10.1162/nol.a.18 · Cited by 2
“According to the DIVA model (Tourville & Guenther, 2011), which is another computational framework that explains how the brain coordinates speech through sensory feedback and motor planning, RCbm is implicated in optimizing motor programs for frequently produced speech sequences.”
Self-citationAlterations of the neural substrate in childhood apraxia of speech: new evidence from neuroimaging
Bosco, P., Podda, I., Cipriano, E., Bombonato, C., Cipriani, P., Bartoli, M., Pasquariello, R., Fiori, S., Biagi, L., & Chilosi, A. M. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf302 · Cited by 2
“7,47,48 Based on the DIVA neurocomputational model, altered somatosensory processing may thus impact speech acquisition.”
Liu, Q., Li, J., Zhao, S., Chen, M., Huang, X., Liu, D., Li, J., Wu, X., Li, Y., Chen, X., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70390 · Cited by 2
Autonomic Arousal During Speech Production and Sensorimotor Adaptation
Tomassi, N. E., Turashvili, D. M., Williams, A., Walsh, B. M., Stephen, E. P., & Stepp, C. E. (2025) · Journal of Speech Language and Hearing Research · doi:10.1044/2025_jslhr-25-00171 · Cited by 2
“In one computational model of speech motor control (Directions Into Velocities of Articulators; Tourville & Guenther, 2011), it is suggested that there are two parallel control processes working together to monitor (feedback) and update (feedforward) speech.”
Basciano, A., Chao, S.-C., Gomez, A., Daliri, A., & Rogalsky, C. (2025) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1672114 · Cited by 2
From Voice to Self: An Integrative Framework on Self-Voice Processing
Orepić, P., & Pinheiro, A. P. (2026) · Perspectives on Psychological Science · doi:10.1177/17456916261422585 · Cited by 2
“For example, the DIVA (Tourville & Guenther, 2011) and the more recent LaDIVA (Weerathunge et al., 2022) frameworks focus on sensorimotor predictions and incorporate feedforward and feedback loops involving auditory, motor, and somatosensory signals.”
The data-driven voice-body in performance: AI voices as materials, mediators, and gifts
Reus, J. C. (2026) · Frontiers in Computer Science · doi:10.3389/fcomp.2026.1686763 · Cited by 3
Vocal wow in an adapted reflex resonance model
Brajot, F.-X., & NEIMAN, A. B. (2020) · The Journal of the Acoustical Society of America · doi:10.1121/10.0000938 · Cited by 2
The integration mechanisms of feedforward and feedback control in speech motor system
Cai, X. F., & Zhang, Q. (2020) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2020.00588 · Cited by 2
“DIVA 模型提出, 刚出生的婴儿不具备言语 产生能力, 语言习得需要经历咿呀学语和模仿两 个发展阶段, 期间均涉及了运动和感觉信息的整 合过程。 咿呀学语阶段。在个体能够产生语音之前 , 必须首先建立起一般性声音(即非特异性语音的 声音)的运动指令和对应感觉反馈之间的联结。在 早期的咿呀学语阶段, 婴儿开始尝试发音, 听觉 和躯体感觉(体觉)皮层分别加工特定发音动作产 生的听觉和体觉反馈信息, 多次尝试后运动和感 觉信息形成配对。此时, 如果听觉或体觉错误发 生时 , 个体可以根据配对后的运动−感觉联结将 感觉错误信号转化为修正性的运动指令(Guenther et al., 2006;…”
InfluentialSíndrome acento extranjero en Esclerosis Múltiple, revisión y presentación de un caso.
Barriga-Maldonado, V., Posadas-Pinto, D. R., Cruz-Guillermo, M., López-Hernández, D. C., & Frenk, I. T. (2021) · Archivos de Neurociencias · doi:10.31157/an.v26i3.225 · Cited by 3
Self-Reported Voice-Related Quality of Life in Cochlear Implant Users
Bottalico, P., Plachno, A., & Nudelman, C. J. (2023) · Revista de Investigación e Innovación en Ciencias de la Salud · doi:10.46634/riics.232 · Cited by 2
“Auditory deprivation and its association with decreased vocal control is directly related to the leading speech production models, such as the Directions Into Velocities of Articulators (DIVA) [45].”
BackgroundDesjardins, M., Jomphe, V., Lagadec-Gaulin, L., Cohen, M. L., & Abbott, K. V. (2024) · Journal of Speech Language and Hearing Research · doi:10.1044/2024_jslhr-23-00673 · Cited by 2
Distinct neural processes link speech planning and execution
Duraivel, S., Rahimpour, S., Barth, K. J., Chiang, C., Wang, C., Harward, S. C., Lad, S. P., Sexton, D., Friedman, A. H., Sinha, S. R., et al. (2026) · Nature Human Behaviour · doi:10.1038/s41562-026-02546-w · Cited by 3
“The DIVA model provides a neuroanatomically explicit framework for speech motor control through parallel feedforward and feedback systems, mapping abstract speech representations to articulatory movements with error correction via auditory and somatosensory feedback 27 .”
InfluentialSpeaking to a metronome reduces kinematic variability in typical speakers and people who stutter
Wiltshire, C. E. E., Cler, G. J., Chiew, M., Freudenberger, J., Chesters, J., Healy, M. P., Hoole, P., & Watkins, K. E. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309612 · Cited by 2
Misspelled‐Word Reading Modulates Late Cortical Dynamics
You, J., Saranpää, A., Lindh‐Knuutila, T., van Vliet, M., & Salmelin, R. H. (2025) · Human Brain Mapping · doi:10.1002/hbm.70247 · Cited by 2
Contextual modulation of language comprehension in a dynamic neural model of lexical meaning
Stern, M. C., & Piñango, M. M. (2025) · Cognition · doi:10.1016/j.cognition.2025.106336 · Cited by 2
The study of speech training and learning method based on DIVA model
Shaobai, Z., & Chenhong, H. (2015) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2015.7260240 · Cited by 2
Are Vowels Normalized After Maxillary Osteotomy? An Acoustic Study in Cleft Lip and Palate
Ho, C. H. Y., Yu, W. S., Tuomainen, J., Sell, D. A., Lee, K., Tong, M. C. F., & Pereira, V. J. (2021) · Journal of Craniofacial Surgery · doi:10.1097/scs.0000000000007650 · Cited by 2
“These internal subsystems integrate the time-varying sensory expectation and the auditory consequences of the active articulatory motor commands to produce and maintain targeted speech patterns.(35) In other words, when a speech sound is produced, the brain would formulate corresponding sensory and auditory expectatio…”
BackgroundWhitfield, J. A., & Holdosh, S. R. (2021) · Journal of Speech Language and Hearing Research · doi:10.1044/2021_jslhr-20-00605 · Cited by 2
Jenson, D. E., & Saltuklaroglu, T. (2022) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2022.108388 · Cited by 2
“Neuroimaging studies over the last two decades have clearly identified the dorsal stream (DS) as an essential network for providing sensorimotor control for speech production (Tourville and Guenther, 2011; Guenther, 2016) and perception (Hickok and Poeppel, 2004, 2007).”
Cerňak, M., Asaei, A., & Hyafil, A. (2016) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 2
Neurocorrelates of speech-motor planning and execution in adults and children who stutter
Brown, B. T., Zebrowski, P. M., Spencer, J. S., Gordon, J. K., Finnegan, E., & Duff, M. (2015) · doi:10.17077/etd.3ijuuip5 · Cited by 2
Jackson, E. S. (2015) · CUNY Academic Works (City University of New York) · Cited by 2
Hertrich, I., Ackermann, H., & Ziegler, W. (2021) · doi:10.1002/9781119606987.ch16 · Cited by 2
Children’s Use of Syntax In Word Learning
Lidz, J. L. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.10 · Cited by 2
Phonological Abstraction in The Mental Lexicon
Bakovic, E. J., Heinz, J. N., & Rawski, J. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198845003.013.14 · Cited by 2
Guldner, S., Nees, F., Flor, H., & McGettigan, C. (2022) · doi:10.31234/osf.io/s549b · Cited by 2
Speaking to a metronome reduces kinematic variability in typical speakers and people who stutterPreprint
Wiltshire, C. E. E., Cler, G. J., Chiew, M., Freudenberger, J., Chesters, J., Healy, M. P., Hoole, P., & Watkins, K. E. (2023) · doi:10.31219/osf.io/wc29m · Cited by 2
Speech-induced suppression and vocal feedback sensitivity in human cortexPreprint
Özker, M., Yu, L., Dugan, P. C., Doyle, W., Friedman, D. J., Devinsky, O., & Flinker, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.08.570736 · Cited by 2
Articulatory Phonology and Speech Impairment
Hagedorn, C., & Namasivayam, A. K. (2024) · doi:10.1002/9781119875949.ch24 · Cited by 2
Active inference and speech motor control: a review and theoryPreprint
Bradshaw, A. R., Press, C., & Davis, M. H. (2024) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/eq4kh · Cited by 4
Manes, J. L., & Guenther, F. H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-820480-1.00120-0 · Cited by 2
Self-citationFunctional Roles of Sensorimotor Alpha and Beta Oscillations in Overt Speech ProductionPreprint
Huang, L. Z., Cao, Y., Janse, E., & Piai, V. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.04.611312 · Cited by 2
“Speech production models have been primarily focused on the spatial location of processes (Hickok, 2012, 2014; Tourville & Guenther, 2011) drawing upon fMRI findings.”
Background
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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