From Analogue to Digital Vocalizations

Sound is a medium used by humans to carry information. The existence of this kind of medium is a pre-requisite for language. It is organized into a code, called speech, which provides a repertoire of forms that is shared in each language community. This 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 characterized by several properties: speech is digital and compositional (vocalizations are made of units re-used systematically in other syllables); phoneme inventories have precise regularities as well as great diversity in human languages; all the speakers of a language community categorize sounds in the same manner, but each language has its own system of categorization, possibly very different from every other. How can a speech code with these properties form? These are the questions we will approach in the paper. We will study them using the method of the artificial. We will build a society of artificial agents, and study what mechanisms may provide answers. This will not prove directly what mechanisms were used for humans, but rather give ideas about what kind of mechanism may have been used. This allows us to shape the search space of possible answers, in particular by showing what is sufficient and what is not necessary. The mechanism we present 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 allows a population of agents to build a speech code that has the properties mentioned above. The originality is that it pre-supposes neither a functional pressure for communication, nor the ability to have coordinated social interactions (they do not play language or imitation games). It relies on the self-organizing properties of a generic coupling between perception and production both within agents, and on the interactions between agents

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

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

The synthetic modeling of language origins

This paper surveys work on the computational modeling of the origins and evolution of language. The main approaches are described and some example experiments from the domains of the evolution of communication, phonetics, lexicon formation, and syntax are discussed.The writing of this paper was sponsored by the Sony Computer Science Laboratory in Paris.Peer reviewe

Artificial Creative Systems: Completing the Creative Cycle

Human creativity is personally, socially and culturally situated: creative individuals work within environments rich in personal experiences, social relationships and cultural knowledge. Computational models of creative processes typically neglect some or all of these aspects of human creativity. How can we hope to capture this richness in computational models of creativity? This paper introduces recent work at the Design Lab where we are attempting to develop a model of artificial creative systems that can combine important aspects at personal, social and cultural levels

Towards a clinical assessment of acquired speech dyspraxia.

No standardised assessment exists for the recognition and quantification of acquired speech dyspraxia (also called apraxia of speech, AS). This thesis aims to work towards development of such an assessment based on perceptual features. Review of previous features claimed to characterise AS and differentiate it from other acquired pronunciation problems (dysarthrias; phonemic paraphasia - PP) has proved negative. Reasons for this have been explored. A reconceptualisation of AS is attempted based on physical studies of AS, PP and the dysarthrias; their position and relationship within coalitional models of speech production; by comparison with normal action control and other dyspraxias. Contrary to the view of many it is concluded that AS and PP are dyspraxias (albeit different types). However, due to the interactive nature of speech-language production and behaviour of the vocal tract as a functional whole AS is unlikely to be distinguishable in an absolute fashion based on single speech characteristics. Rather it is predicted that pronunciation disordered groups will differ relatively on total error profiles and susceptibility to associated effects (variability; propositionality; struggle; length-complexity; latency-utterance times). Using a prototype battery and refined error transcription and analysis procedures a series of studies test predictions on three groups: spastic dysarthrics (n = 6) AS and PP without (n = 12) and with (n = 12) dysphasia. The main conclusions do not support the error profile hypotheses in any straightforward manner. Length-complexity effects and latency-utterance times fail to consistently separate groups. Variability, propositionality and struggle proved the most reliable indicators. Error profiles remain the closest indicators of speakers' intelligibility and therapeutic goals. The thesis argues for a single case approach to differential diagnosis and alternative statistical analyses to capture individual and group differences. Suggestions for changes to the prototype clinical battery and data management to effect optimal speaker differentiation conclude the work