Speech Development by Imitation

The Double Cone Model (DCM) is a model of how the brain transforms sensory input to motor commands through successive stages of data compression and expansion. We have tested a subset of the DCM on speech recognition, production and imitation. The experiments show that the DCM is a good candidate for an artificial speech processing system that can develop autonomously. We show that the DCM can learn a repertoire of speech sounds by listening to speech input. It is also able to link the individual elements of speech to sequences that can be recognized or reproduced, thus allowing the system to imitate spoken language

PRESENCE: A human-inspired architecture for speech-based human-machine interaction

Recent years have seen steady improvements in the quality and performance of speech-based human-machine interaction driven by a significant convergence in the methods and techniques employed. However, the quantity of training data required to improve state-of-the-art systems seems to be growing exponentially and performance appears to be asymptotic to a level that may be inadequate for many real-world applications. This suggests that there may be a fundamental flaw in the underlying architecture of contemporary systems, as well as a failure to capitalize on the combinatorial properties of human spoken language. This paper addresses these issues and presents a novel architecture for speech-based human-machine interaction inspired by recent findings in the neurobiology of living systems. Called PRESENCE-"PREdictive SENsorimotor Control and Emulation" - this new architecture blurs the distinction between the core components of a traditional spoken language dialogue system and instead focuses on a recursive hierarchical feedback control structure. Cooperative and communicative behavior emerges as a by-product of an architecture that is founded on a model of interaction in which the system has in mind the needs and intentions of a user and a user has in mind the needs and intentions of the system

A tale of two lexica: Investigating computational pressures on word representation with neural networks

IntroductionThe notion of a single localized store of word representations has become increasingly less plausible as evidence has accumulated for the widely distributed neural representation of wordform grounded in motor, perceptual, and conceptual processes. Here, we attempt to combine machine learning methods and neurobiological frameworks to propose a computational model of brain systems potentially responsible for wordform representation. We tested the hypothesis that the functional specialization of word representation in the brain is driven partly by computational optimization. This hypothesis directly addresses the unique problem of mapping sound and articulation vs. mapping sound and meaning.ResultsWe found that artificial neural networks trained on the mapping between sound and articulation performed poorly in recognizing the mapping between sound and meaning and vice versa. Moreover, a network trained on both tasks simultaneously could not discover the features required for efficient mapping between sound and higher-level cognitive states compared to the other two models. Furthermore, these networks developed internal representations reflecting specialized task-optimized functions without explicit training.DiscussionTogether, these findings demonstrate that different task-directed representations lead to more focused responses and better performance of a machine or algorithm and, hypothetically, the brain. Thus, we imply that the functional specialization of word representation mirrors a computational optimization strategy given the nature of the tasks that the human brain faces

Biological and Cognitive Plausibility in Connectionist Networks for Language Modeling

If we want to explain cognitive processes with means of connectionist networks, these networks have to correspond with cognitive systems and their underlying biological mechanisms in different respects. The question of biological and cognitive plausibility of connectionist models arises from two different aspects – first, from the aspect of biology – on one hand, one has to have a fair understanding of biological mechanisms and cognitive mechanisms in order to represent them in a model, and on the other hand there is the aspect of modeling – one has to know how to construct a model to represent precisely what we are aiming at. Computer power and modeling techniques have improved dramatically in recent 20 years, so the plausibility problem is being addressed in more adequate ways as well. Connectionist models are often used for representing different aspects of natural language. Their biological plausibility had sometimes been questioned in the past. Today, the field of computational neuroscience offers several acceptable possibilities of modeling higher cognitive functions, and language is among them. This paper brings a presentation of some existing connectionist networks modeling natural language. The question of their explanatory power and plausibility in terms of biological and cognitive systems they are representing is discussed

Neural Network Models for Language Acquisition: A Brief Survey

Abstract. Since the outbreak of connectionist modelling in the mid eighties, several problems in natural language processing have been tackled by employing neural network-based techniques. Neural network's biological plausibility oers a promising framework in which the computational treatment of language may be linked to other disciplines such as cognitive science and psychology. With this brief survey, we set out to explore the landscape of articial neural models for the acquisition of language that have been proposed in the research literature

Brain-Language Research: Where is the Progress?

Recent cognitive neuroscience research improved our understanding of where, when, how, and why language circuits emerge and activate in the human brain. Where: Regions crucial for very specific linguistic processes were delineated; phonetic features and fine semantic categories could be mapped onto specific sets of cortical areas. When: Brain correlates of phonological, syntactic and semantic processes were documented early-on, suggesting language understanding in an instant (within 250ms). How: New mechanistic network models mimicking structure and function of left-perisylvian language areas suggest that multimodal action-perception circuits — rather than separate modules for action and perception — carry the processing resources for language use and understanding. Why language circuits emerge in specific areas, become active at specific early time points and are connected in specific ways is best addressed in light of neuroscience principles governing neuronal activation, correlation learning, and, critical-ly, partly predetermined structural information wired into connections between cortical neurons and areas

Transformation of a temporal speech cue to a spatial neural code in human auditory cortex

In speech, listeners extract continuously-varying spectrotemporal cues from the acoustic signal to perceive discrete phonetic categories. Spectral cues are spatially encoded in the amplitude of responses in phonetically-tuned neural populations in auditory cortex. It remains unknown whether similar neurophysiological mechanisms encode temporal cues like voice-onset time (VOT), which distinguishes sounds like /b/ and/p/. We used direct brain recordings in humans to investigate the neural encoding of temporal speech cues with a VOT continuum from /ba/ to /pa/. We found that distinct neural populations respond preferentially to VOTs from one phonetic category, and are also sensitive to sub-phonetic VOT differences within a population’s preferred category. In a simple neural network model, simulated populations tuned to detect either temporal gaps or coincidences between spectral cues captured encoding patterns observed in real neural data. These results demonstrate that a spatial/amplitude neural code underlies the cortical representation of both spectral and temporal speech cues

Modeling speech processing in case of neurogenic speech and language disorders: neural dysfunctions, brain lesions, and speech behavior

Computer-implemented neural speech processing models can simulate patients suffering from neurogenic speech and language disorders like aphasia, dysarthria, apraxia of speech, and neurogenic stuttering. Speech production and perception tasks simulated by using quantitative neural models uncover a variety of speech symptoms if neural dysfunctions are inserted into these models. Neural model dysfunctions can be differentiated with respect to type (dysfunction of neuron cells or of neural connections), location (dysfunction appearing in a specific buffer of submodule of the model), and severity (percentage of affected neurons or neural connections in that specific submodule of buffer). It can be shown that the consideration of quantitative computer-implemented neural models of speech processing allows to refine the definition of neurogenic speech disorders by unfolding the relation between inserted neural dysfunction and resulting simulated speech behavior while the analysis of neural deficits (e.g., brain lesions) uncovered from imaging experiments with real patients does not necessarily allow to precisely determine the neurofunctional deficit and thus does not necessarily allow to give a precise neurofunctional definition of a neurogenic speech and language disorder. Furthermore, it can be shown that quantitative computer-implemented neural speech processing models are able to simulate complex communication scenarios as they appear in medical screenings, e.g., in tasks like picture naming, word comprehension, or repetition of words or of non-words (syllable sequences) used for diagnostic purposes or used in speech tasks appearing in speech therapy scenarios (treatments). Moreover, neural speech processing models which can simulate neural learning are able to simulate progress in the overall speech processing skills of a model (patient) resulting from specific treatment scenarios if these scenarios can be simulated. Thus, quantitative neural models can be used to sharpen up screening and treatment scenarios and thus increase their effectiveness by varying certain parameters of screening as well as of treatment scenarios

Respiratory, postural and spatio-kinetic motor stabilization, internal models, top-down timed motor coordination and expanded cerebello-cerebral circuitry: a review

Human dexterity, bipedality, and song/speech vocalization in Homo are reviewed within a motor evolution perspective in regard to 

(i) brain expansion in cerebello-cerebral circuitry, 
(ii) enhanced predictive internal modeling of body kinematics, body kinetics and action organization, 
(iii) motor mastery due to prolonged practice, 
(iv) task-determined top-down, and accurately timed feedforward motor adjustment of multiple-body/artifact elements, and 
(v) reduction in automatic preflex/spinal reflex mechanisms that would otherwise restrict such top-down processes. 

Dual-task interference and developmental neuroimaging research argues that such internal modeling based motor capabilities are concomitant with the evolution of 
(vi) enhanced attentional, executive function and other high-level cognitive processes, and that 
(vii) these provide dexterity, bipedality and vocalization with effector nonspecific neural resources. 

The possibility is also raised that such neural resources could 
(viii) underlie human internal model based nonmotor cognitions. 
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A Biopsychological Foundation for Linguistics

In this dissertation, I defend the view that natural languages are concrete biopsychological phenomena to be studied empirically. In Section One, I begin with an historical explanation. Some analytic philosophers, I argue, misapply formal logic as an analysis of natural language, when it was in fact originally developed as an alternative to natural language, employed for scientific purposes. Abstract, quasi-mathematical philosophies of language, I argue, are partially a result of this misunderstanding. I respond to Jerrold Katz’ argument that a proper understanding of analytic truth requires this quasi-mathematical philosophy of language through a model-theoretical analysis of analytic truth in modal and intuitionist logics. In Section Two, I offer a positive argument for a biopsychological philosophy of language. While Chomsky and others have emphasized the metaphysical basis of natural languages in psychological representations, I further contribute to understanding by emphasizing the basis of natural language in psychological representations of relevant properties of a specifically constrained biological implementation base. I defend this ontological perspective through a thorough engagement with the subfield of linguistic phonology and its important relations to physiological articulation and perception, along with an analysis of crucial interface relations among phonology, morphology and syntax. In the final section, I engage with the objections to this biopsychological philosophy of language stemming from concerns related to linguistic normativity and communication. If natural language is based metaphysically in the biopsychological representations of individuals, there are apparent paradoxes in the notion of public rules for language use, and in the notion of shared content for the purpose of communication. Drawing on David Forrest Wallace’s pragmatic conception of linguistic prescription, together with analogies from anti-realist metaethical systems, I defend the intelligibility of public linguistics norms without the need for abstract ontological commitment. Drawing on Ray Jackendoff’s internalist semantic and metasemantic analysese, together with Burtrand Russell’s analogy argument on other minds, I also defend intelligibility of linguistic communication equally without need for abstract ontological commitment