EPJ Nonlinear Biomed Phys
Volume 2, Number 1, December 2014
|Number of page(s)||28|
|Published online||10 February 2014|
Associative learning and self-organization as basic principles for simulating speech acquisition, speech production, and speech perception
Neurophonetics Group, Department of Phoniatrics, Pedaudiology, and Communication Disorders, Medical School, RWTH Aachen University, Aachen, Germany
2 Cognitive Computation and Applications Laboratory, School of Computer Science and Technology, Tianjin University, Tianjin, China
3 Education and Rehabilitation of the Deaf and Hard of Hearing, Department of Special Education, Faculty of Human Sciences, University of Cologne, Cologne, Germany
* e-mail: firstname.lastname@example.org
Accepted: 18 December 2013
Published online: 10 February 2014
Quantitative neural models of speech acquisition and speech processing are rare.
In 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).
The 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.
This quantitative model gives an intuitive understanding of basic neurobiological principles from the viewpoint of speech acquisition and speech processing.
Key words: Speech production / Speech perception / Speech acquisition / Babbling / Imitation / Associative learning / Self-organization / Neural maps / Self-organizing maps / Sensorimotor learning
© The Author(s), 2014