Towards the Acquisition of a Sensorimotor Vocal Tract Action Repository within a Neural Model of Speech Processing

Kröger, Bernd J.; Birkholz, Peter; Kannampuzha, Jim; Kaufmann, Emily; Neuschaefer‐Rube, Christiane

doi:10.1007/978-3-642-25775-9_27

Cited by 10 publications

(23 citation statements)

References 11 publications

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“…As described in Kröger and his colleagues' language acquisition model (Kröger and Heim, 2011; Kröger et al, 2011a,b), auditory information and semantic information are acquired at two different levels. Therefore, two separate maps (GSOMs) are used to model the acquisition of auditory information and semantic information separately.…”

Section: Discussionmentioning

confidence: 99%

Interconnected growing self-organizing maps for auditory and semantic acquisition modeling

Cao

Fang

et al. 2014

Front. Psychol.

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Based on the incremental nature of knowledge acquisition, in this study we propose a growing self-organizing neural network approach for modeling the acquisition of auditory and semantic categories. We introduce an Interconnected Growing Self-Organizing Maps (I-GSOM) algorithm, which takes associations between auditory information and semantic information into consideration, in this paper. Direct phonetic–semantic association is simulated in order to model the language acquisition in early phases, such as the babbling and imitation stages, in which no phonological representations exist. Based on the I-GSOM algorithm, we conducted experiments using paired acoustic and semantic training data. We use a cyclical reinforcing and reviewing training procedure to model the teaching and learning process between children and their communication partners. A reinforcing-by-link training procedure and a link-forgetting procedure are introduced to model the acquisition of associative relations between auditory and semantic information. Experimental results indicate that (1) I-GSOM has good ability to learn auditory and semantic categories presented within the training data; (2) clear auditory and semantic boundaries can be found in the network representation; (3) cyclical reinforcing and reviewing training leads to a detailed categorization as well as to a detailed clustering, while keeping the clusters that have already been learned and the network structure that has already been developed stable; and (4) reinforcing-by-link training leads to well-perceived auditory–semantic associations. Our I-GSOM model suggests that it is important to associate auditory information with semantic information during language acquisition. Despite its high level of abstraction, our I-GSOM approach can be interpreted as a biologically-inspired neurocomputational model.

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Section: Discussionmentioning

confidence: 99%

Interconnected growing self-organizing maps for auditory and semantic acquisition modeling

Cao

Fang

et al. 2014

Front. Psychol.

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“…In the theoretical computer-implemented neurofunctional speech model of Kröger et al (2009, 2011) the close relationship of speech production and speech perception is postulated as mentioned by Liberman et al (1967), Liberman and Mattingly (1985), or Fowler (1986). Moreover the speech–action-repository (SAR) is assumed to be a neurofunctional model of non-symbolic (i.e., without semantics), supramodal (i.e., modality independent) syllable processing, which integrates higher-level (i.e., cortical) sensorimotor representations.…”

Section: Introductionmentioning

confidence: 99%

“…Riecker et al, 2005). The SAR model is based on simulation experiments (Kröger et al, 2009, 2011) that integrated an associative and self-organizing neural network approach (Kohonen, 2001) comprising two kinds of maps, i.e., a neural self-organizing map and neural state maps. Each of these maps comprises neurons, which represent different syllabic information (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…Each of these maps comprises neurons, which represent different syllabic information (see Figure 1). Within the SAR model it is assumed that the syllabary is a supramodal hub linking motor and sensory (somatosensory and auditory) higher-level representations of frequent syllables (Kröger et al, 2011), which involves a brain network rather than one single region. In the current SAR approach, the syllabary not just stores a motor plan (gesture scores) for each frequent syllable.…”

Section: Introductionmentioning

confidence: 99%

“…The sensorimotor knowledge is stored by synaptic link weights, i.e., neural mappings, between neurons of the phonetic map and neurons of the state maps, i.e., motor plan map, auditory map, and somatosensory map, hosting motor and sensory (somatosensory and auditory) representations of a syllable, if it is activated. The supramodal phonetic map is self-organizing and this map and its mappings toward the motor and sensory state maps are trained during speech acquisition (Kröger et al, 2009, 2011). Therefore, the phonetic map as well as the mappings toward motor and sensory maps can be interpreted as a part of long-term memory while the motor and sensory state maps are interpreted as parts of short-term memory (ibid.…”

Section: Introductionmentioning

confidence: 99%

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Neural representation of the sensorimotor speech–action-repository

Eckers¹,

Kröger²,

Saß³

et al. 2013

Front. Hum. Neurosci.

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A speech–action-repository (SAR) or “mental syllabary” has been proposed as a central module for sensorimotor processing of syllables. In this approach, syllables occurring frequently within language are assumed to be stored as holistic sensorimotor patterns, while non-frequent syllables need to be assembled from sub-syllabic units. Thus, frequent syllables are processed efficiently and quickly during production or perception by a direct activation of their sensorimotor patterns. Whereas several behavioral psycholinguistic studies provided evidence in support of the existence of a syllabary, fMRI studies have failed to demonstrate its neural reality. In the present fMRI study a reaction paradigm using homogeneous vs. heterogeneous syllable blocks are used during overt vs. covert speech production and auditory vs. visual presentation modes. Two complementary data analyses were performed: (1) in a logical conjunction, activation for syllable processing independent of input modality and response mode was assessed, in order to support the assumption of existence of a supramodal hub within a SAR. (2) In addition priming effects in the BOLD response in homogeneous vs. heterogeneous blocks were measured in order to identify brain regions, which indicate reduced activity during multiple production/perception repetitions of a specific syllable in order to determine state maps. Auditory-visual conjunction analysis revealed an activation network comprising bilateral precentral gyrus (PrCG) and left inferior frontal gyrus (IFG) (area 44). These results are compatible with the notion of a supramodal hub within the SAR. The main effect of homogeneity priming revealed an activation pattern of areas within frontal, temporal, and parietal lobe. These findings are taken to represent sensorimotor state maps of the SAR. In conclusion, the present study provided preliminary evidence for a SAR.

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The Neurophonetic Model of Speech Processing ACT: Structure, Knowledge Acquisition, and Function Modes

Kröger

Kannampuzha

Eckers

et al. 2012

Cognitive Behavioural Systems

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Towards the Acquisition of a Sensorimotor Vocal Tract Action Repository within a Neural Model of Speech Processing

Cited by 10 publications

References 11 publications

Interconnected growing self-organizing maps for auditory and semantic acquisition modeling

Interconnected growing self-organizing maps for auditory and semantic acquisition modeling

Neural representation of the sensorimotor speech–action-repository

The Neurophonetic Model of Speech Processing ACT: Structure, Knowledge Acquisition, and Function Modes

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