Abstract
BackgroundQuantitative neural models of speech acquisition and speech processing are rare.MethodsIn this paper, we describe a neural model for simulating speech acquisition, speech production, and speech perception. The model is based on two important neural features: associative learning and self-organization. The model describes an SOM-based approach to speech acquisition, i.e. how speech knowledge and speaking skills are learned and stored in the context of self-organizing maps (SOMs).ResultsThe model elucidates that phonetic features, such as high-low, front-back in the case of vowels, place and manner or articulation in the case of consonants and stressed vs. unstressed for syllables, result from the ordering of syllabic states at the level of a supramodal phonetic self-organizing map. After learning, the speech production and speech perception of speech items results from the co-activation of neural states within different cognitive and sensorimotor neural maps.ConclusionThis quantitative model gives an intuitive understanding of basic neurobiological principles from the viewpoint of speech acquisition and speech processing.
Highlights
Quantitative neural models of speech acquisition and speech processing are rare
Because imitation always requires some previous sensorimotor knowledge, in our model, babbling training is performed before imitation training
It is important to note that the language-specific speech items spoken by the caretaker normally guide babbling: the set of all possible motor plan states which could be generated randomly would be infinitely huge and could not be trained by the model
Summary
Quantitative neural models of speech acquisition and speech processing are rare. While a great deal of research has been carried out in order to investigate brain locations of different parts or modules which comprise the speech production and speech perception system (e.g. [1,2,3]), little is known about the neural functioning of these modules during speech acquisition, speech production, and speech perception. The neuroanatomically grounded Hebbian-learning model [8], establishes highly specialized functional units called “Hebbian neuronal circuits” (HNCs, see [9]). This model appears to be especially neurobiologically realistic since it learns to associate sensory and motor speech items in a similar way to the early phases of speech acquisition in children. Self-organizing map approaches (SOM, Kohonen) belong to the group of lumped element rate based approaches as well, but it should be noted that the degree of abstraction is much higher in SOM models than in models such as the neuroanatomically grounded Hebbian-learning model of [8]. SOM approaches – as well as more neuroantomically grounded approaches – are capable of representing the basic principles of neural systems, i.e. self-organization, associative learning, Hebbian learning, adaptation, and neural plasticity
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