Associative learning and self-organization as basic principles for simulating speech acquisition, speech production, and speech perception

Abstract

Background: Quantitative neural models of speech acquisition and speech processing are rare. Methods: 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). Results: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.Conclusion: This quantitative model gives an intuitive understanding of basic neurobiological principles from the viewpoint of speech acquisition and speech processing.

A methodology for monitoring emotional stress in phonation

“…Just to present a clear paradigm well known in the field of Automatic Speech Recognition, where a lot of bio-inspired knowledge has been integrated, and a lot more will be in the next years, the pioneer paper by Hermansky is to be cited [14]. Since then, many researchers have oriented their work to better understand natural cognitive systems to extract useful knowledge to be integrated in Speech Production and Perception [15]. In this line our research group has carried out a series of studies to explore the field of Speech Perception under the bioinspired point of view [16].…”

Goal babbling of acoustic-articulatory models with adaptive exploration noise

“…To account for cases where several productions are unsuccessful (e.g. if no phonation occurs), a threshold of 1.5 is defined in (7).…”

“…Accordingly, many developmental models of speech acquisition implement vocal learning as an imitative process [6], [7], [8]. Applying active motor exploration, these studies acquire the articulatory configurations to successfully imitate a set of speech sounds in a babbling phase.…”

“…The Elija model [10] removes redundancies after the babbling phase by consolidating the learned motor patterns based on the acoustic consequences. [6] and [7] connect articulation and acoustics via a map of acquired speech sounds.…”

“…Although it has been used for neurocomputational modeling of speech acquisition ( [22], [7]), goal babbling has not yet been applied to this vocal tract model. Recently, Murakami et al [8] applied reinforcement learning to learn the vowels [a], [i] and [u] with VTL.…”