Assessing Idiosyncrasies in a Bayesian Model of Speech Communication

International audienceAlthough speakers of one specific language share the same phoneme representations, their productions can differ. We propose to investigate the development of these differences in production , called idiosyncrasies, by using a Bayesian model of communication. Supposing that idiosyncrasies appear during the development of the motor system, we present two versions of the motor learning phase, both based on the guidance of an agent master: " a repetition model " where agents try to imitate the sounds produced by the master and " a communication model " where agents try to replicate the phonemes produced by the master. Our experimental results show that only the " communication model " provides production idiosyncrasies, suggesting that idiosyncrasies are a natural output of a motor learning process based on a communicative goal

Modeling the concurrent development of speech perception and production in a Bayesian framework: COSMO, a Bayesian computational model of speech communication: Assessing the role of sensory vs. motor knowledge in speech perception

International audienceIt is now widely accepted that there is a functional relationship between the speech perception and production systems in the human brain. However, the precise mechanisms and role of this relationship still remain debated. The question of invariance and robustness in categorization are set at the center of the debate: how is stable information extracted from the variable sensory input in order to achieve speech comprehension? In this context, auditory (resp. motor, perceptuo-motor) theories propose that speech is categorized thanks to auditory (resp. motor, perceptuo-motor) processes. However, experimental evidence is still scarce and does not allow to clearly distinguish between the current theories and determine whether invariance in speech perception is of an auditory or motor type. This is why we developed COSMO, a Bayesian model comparing sensory and motor processes in the form of probability distributions which enable both theoretical developments and quantitative simulations. A first significant result in COSMO is an indistinguishability theorem: it is only by simulations of adverse conditions or partial learning that the specificity of sensory vs. motor processing can emerge and provide a basis for evaluation of the specific role of each sub-system. We present the COSMO model, and how its sensory and motor sub-systems are learned, then we describe simulations exploring the way these sub-systems differ during speech categorization. We discuss the experimental results in the light of a “narrowband vs. wideband” interpretation: the sensory sub-system is more precisely tuned to the frequently learned sensory input and hence more efficient in recognizing these inputs, providing a “narrowband” system. Conversely, the motor sub-system is less accurate to recognize learned sensory inputs but it has better generalization properties, making it more robust to unexpected variability which would provide it with “wideband” characteristics

A learning perspective on the emergence of abstractions:the curious case of phonemes

In the present paper we use a range of modeling techniques to investigate whether an abstract phone could emerge from exposure to speech sounds. In effect, the study represents an attempt for operationalize a theoretical device of Usage-based Linguistics of emergence of an abstraction from language use. Our quest focuses on the simplest of such hypothesized abstractions. We test two opposing principles regarding the development of language knowledge in linguistically untrained language users: Memory-Based Learning (MBL) and Error-Correction Learning (ECL). A process of generalization underlies the abstractions linguists operate with, and we probed whether MBL and ECL could give rise to a type of language knowledge that resembles linguistic abstractions. Each model was presented with a significant amount of pre-processed speech produced by one speaker. We assessed the consistency or stability of what these simple models have learned and their ability to give rise to abstract categories. Both types of models fare differently with regard to these tests. We show that ECL models can learn abstractions and that at least part of the phone inventory and grouping into traditional types can be reliably identified from the input.Comment: 36 page

Statistical Learning of Prosodic Patterns and Reversal of Perceptual Cues forSentence Prominence

Recent work has proposed that prominence perception inspeech could be driven by predictability of prosodic patterns,connecting prominence perception to the concept of statisticallearning. In the present study, we tested the predictabilityhypothesis by conducting a listening test where subjects werefirst exposed to a 5-minute stream of sentences with a certainproportion of sentence-final words having either a falling orrising pitch trajectory. After the exposure stage, subjects wereasked to grade prominence in a set of novel sentences withsimilar pitch patterns. The results show that the subjects weresignificantly more likely to perceive words with low-probability pitch trajectories as prominent independently ofthe direction of the pitch change. This suggests that evenshort exposure to prosodic patterns with a certain statisticalstructure can induce changes in prominence perception,supporting the connection between prominence perceptionand attentional orientation towards low-probability events inan otherwise predictable context

The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling

Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling