This paper describes a neural model of speech acquisition and production that accounts for a wide range of acoustic, kinematic, and neuroimaging data concerning the control of speech movements. The model is a neural network whose components correspond to regions of the cerebral cortex and cerebellum, including premotor, motor, auditory, and somatosensory cortical areas. Computer simulations of the model verify its ability to account for compensation to lip and jaw perturbations during speech. Specific anatomical locations of the model's components are estimated, and these estimates are used to simulate fMRI experiments of simple syllable production. 1 The advent of functional brain imaging techniques that are safe for use on human subjects has led to an explosion in the amount of data concerning brain activity during speech and language tasks. The current article details a neural model of speech production that provides a conceptual and computational framework for interpreting many of these datasets. The model is a neural network model of speech acquisition and production, called the DIVA model (Directions Into Velocities of Articulators), that utilizes a babbling cycle to learn to control movements of simulated speech articulators in order to produce phoneme strings. Over the past decade, our laboratory has used numerical simulations to show how the model provides a relatively simple, unified account of a very wide range of speech production phenomena, including motor equivalence, contextual variability, anticipatory and carryover coarticulation, velocity/distance relationships, speaking rate effects, and speaking skill acquisition (e.g., Guenther, 1994; Guenther, 1995; Guenther, Hampson, & Johnson, 1998; Nieto-Castanon, in press). Predictions concerning speech production in normal adults have been drawn from the model and tested using electromagnetic articulometry (e.g., Guenther et al., 1999; Perkell et al., 2004). The model has been used to account for issues in child development (e.g., Guenther, 1995) , including a demonstration of its ability to deal with the dramatic changes in size and shape of the speech articulators that take place during the first three years of life (Callan, Kent, Guenther, & Vorperian, 2000) . The model has also been used to investigate the role of auditory feedback in speech production in normally hearing individuals, deaf individuals, and individuals who have recently regained some hearing through the use of cochlear implants (Perkell et al., 2000) , and to investigate stuttering (Max, Guenther, Gracco, Ghosh, & Wallace, 2004) . Because the DIVA model is defined as a neural network, its components can be interpreted in terms of brain function in a straightforward way. The model thus provides an ideal framework for interpreting data from functional imaging studies of the human brain during speech tasks. Preliminary associations of the model's components with specific brain regions have been presented elsewhere (e.g., Guenther, 1998; Guenther, 2001; Guenther et al., 2003) ; a primary goal of the current paper is to provide a more thorough treatment of the hypothesized neural bases of the model's components. A second purpose of the current work is to extend the model to incorporate realistic neural processing delays, and therefore more realistically address the issue of combining feedforward and feedback control strategies. Earlier versions of the DIVA model effectively assumed instantaneous transmission of neural signals. However the nervous system must cope with potentially destabilizing delays in the control of articulator movements. For example, a motor command generated in the primary motor cortex will typically take 40 ms or more before it effects movement of the associated speech articulator. Similarly, sensory information from the articulators and cochlea are delayed by tens of ms before they reach the primary sensory cortices. These transmission delays can be very problematic for a system that must control the rapid articulator movements underlying speech. Most adults can pronounce the word "dilapidated" in less than one second; this word requires 10 transitions between phonemes, with each transition taking less than 100ms to complete. A purely feedback-based control system faced with the delays mentioned above would not be able to stably produce speech at this rate. Instead, our speech production system must supplement feedback control with feedforward control mechanisms. In this article we address the integration of feedback and feedforward control subsystems in the control of speech movements with realistic processing delays, and we provide model simulations of perturbation studies that probe the temporal response properties of feedback control mechanisms. Several aspects of the DIVA model differentiate it from other models in the speech production literature (e.g., Levelt, Roelofs, & Meyer, 1999; Saltzman & Munhall, 1989; Morasso, Sanguineti, & Frisone, 2001; Westermann & Reck, 2004) . Whereas the Levelt, Roelofs, & Meyer (1999) model focuses on linguistic and phonological computations down to the syllable level, the DIVA model focuses on the sensorimotor transformations underlying the control of articulator movements. Thus, the DIVA model focuses on speech control at the syllable and lower motor levels. The task dynamic model of Saltzman et al. (1989) is a computational model that provides an alternative account of the control of articulator movements. However, unlike the DIVA model its components are not associated with particular brain regions, neuron types, or synaptic pathways. Of current biologically plausible neural network models of speech production (e.g., Morasso et al., 2001; Westermann et al., 2004) , the DIVA model is the most thoroughly defined and tested, and it is unique in using a pseudoinverse-style control scheme (from which the model's name is derived) that has been shown to provide accurate accounts of human articulator kinematic data (e.g., Guenther et al., 1998; Guenther et al., 1999; Nieto Castanon et al., in press). It is also unique in using a combination of feedback and feedforward control mechanisms (as described in the current article), as well as embodying a convex region theory for the targets of speech that has been shown to provide a unified account of a wide body of speech acoustic, kinematic, and EMG data (Guenther, 1995) . An overview of the DIVA model and description of its components are provided in the next section. Subsequent sections relate the model's components to regions of the cerebral cortex and cerebellum, including
Among the efforts to model the acquisition of speech there is the DIVA model REF .
Neural modeling and imaging of the cortical interactions underlying syllable production
Brain and Language
Psychology Medicine