56 research outputs found
The Self-Organization of Speech Sounds
The speech code is a vehicle of language: it defines
a set of forms used by a community to carry information.
Such a code is necessary to support the linguistic
interactions that allow humans to communicate.
How then may a speech code be formed prior to the
existence of linguistic interactions?
Moreover, the human speech code is discrete and compositional,
shared by all the individuals of a community but different
across communities, and phoneme inventories are characterized by
statistical regularities. How can a speech code with these properties form?
We try to approach these questions in the paper,
using the ``methodology of the artificial''. We
build a society of artificial agents, and detail a mechanism that
shows the formation of a discrete speech code without pre-supposing
the existence of linguistic capacities or of coordinated interactions.
The mechanism is based on a low-level model of
sensory-motor interactions. We show that the integration of certain very
simple and non language-specific neural devices
leads to the formation of a speech code that
has properties similar to the human speech code.
This result relies on the self-organizing properties of a generic
coupling between perception and production
within agents, and on the interactions between agents.
The artificial system helps us to develop better intuitions on how speech
might have appeared, by showing how self-organization
might have helped natural selection to find speech
The self-organization of combinatoriality and phonotactics in vocalization systems
This paper shows how a society of agents can self-organize a shared vocalization system that is
discrete, combinatorial and has a form of primitive phonotactics, starting from holistic inarticulate
vocalizations. The originality of the system is that: (1) it does not include any explicit pressure for
communication; (2) agents do not possess capabilities of coordinated interactions, in particular they
do not play language games; (3) agents possess no specific linguistic capacities; and (4) initially
there exists no convention that agents can use. As a consequence, the system shows how a primitive
speech code may bootstrap in the absence of a communication system between agents, i.e. before the
appearance of language
The Effects of Humming and Pitch on Craniofacial and Craniocervical Morphology Measured Using MRI
Peer reviewedPreprin
Using Active Shape Modeling Based on MRI to Study Morphologic and Pitch-Related Functional Changes Affecting Vocal Structures and the Airway
Copyright © 2013 The Voice Foundation. Published by Mosby, Inc. All rights reserved.Peer reviewedPostprin
Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges
Computational models of emergent communication in agent populations are
currently gaining interest in the machine learning community due to recent
advances in Multi-Agent Reinforcement Learning (MARL). Current contributions
are however still relatively disconnected from the earlier theoretical and
computational literature aiming at understanding how language might have
emerged from a prelinguistic substance. The goal of this paper is to position
recent MARL contributions within the historical context of language evolution
research, as well as to extract from this theoretical and computational
background a few challenges for future research
Open challenges in understanding development and evolution of speech forms: The roles of embodied self-organization, motivation and active exploration
This article discusses open scientific challenges for understanding
development and evolution of speech forms, as a commentary to Moulin-Frier et
al. (Moulin-Frier et al., 2015). Based on the analysis of mathematical models
of the origins of speech forms, with a focus on their assumptions , we study
the fundamental question of how speech can be formed out of non--speech, at
both developmental and evolutionary scales. In particular, we emphasize the
importance of embodied self-organization , as well as the role of mechanisms of
motivation and active curiosity-driven exploration in speech formation. Finally
, we discuss an evolutionary-developmental perspective of the origins of
speech
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel
While multi-agent reinforcement learning has been used as an effective means
to study emergent communication between agents, existing work has focused
almost exclusively on communication with discrete symbols. Human communication
often takes place (and emerged) over a continuous acoustic channel; human
infants acquire language in large part through continuous signalling with their
caregivers. We therefore ask: Are we able to observe emergent language between
agents with a continuous communication channel trained through reinforcement
learning? And if so, what is the impact of channel characteristics on the
emerging language? We propose an environment and training methodology to serve
as a means to carry out an initial exploration of these questions. We use a
simple messaging environment where a "speaker" agent needs to convey a concept
to a "listener". The Speaker is equipped with a vocoder that maps symbols to a
continuous waveform, this is passed over a lossy continuous channel, and the
Listener needs to map the continuous signal to the concept. Using deep
Q-learning, we show that basic compositionality emerges in the learned language
representations. We find that noise is essential in the communication channel
when conveying unseen concept combinations. And we show that we can ground the
emergent communication by introducing a caregiver predisposed to "hearing" or
"speaking" English. Finally, we describe how our platform serves as a starting
point for future work that uses a combination of deep reinforcement learning
and multi-agent systems to study our questions of continuous signalling in
language learning and emergence.Comment: 12 pages, 6 figures, 3 tables; under review as a conference paper at
ICLR 202
Multi-Agent Simulation of Emergence of Schwa Deletion Pattern in Hindi
Recently, there has been a revival of interest in multi-agent simulation techniques for exploring the nature of language change. However, a lack of appropriate validation of simulation experiments against real language data often calls into question the general applicability of these methods in modeling realistic language change. We try to address this issue here by making an attempt to model the phenomenon of schwa deletion in Hindi through a multi-agent simulation framework. The pattern of Hindi schwa deletion and its diachronic nature are well studied, not only out of general linguistic inquiry, but also to facilitate Hindi grapheme-to-phoneme conversion, which is a preprocessing step to text-to-speech synthesis. We show that under certain conditions, the schwa deletion pattern observed in modern Hindi emerges in the system from an initial state of no deletion. The simulation framework described in this work can be extended to model other phonological changes as well.Language Change, Linguistic Agent, Language Game, Multi-Agent Simulation, Schwa Deletion
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