The Benue-Gongola-Chad Basin : zone of ethnic and linguistic compression

We wish to emphasize the fact that so far our investigations have concentrated on documenting large bodies of data covering a number of linguistic units in an area which - as we hope to have demonstrated - displays a highly complex linguistic and ethnic structure. Our aim in the above remarks is essentially to throw out a challenge. In order to be able to interpret this situation in terms of the historic development of this zone of compression, further investigations are required, particularly regarding linguistic interference between Chadic and Niger-Congo languages in the south, as well as between Chadic and Nilo-Saharan languages, particularly Kanuri in the north-east and Songhay in the north-west. Ultimately, questions like the following are at stake: To what extent did the numerous Chadic languages preserve their original Hamitosemitic heritage? What is the impact of the Niger-Congo and Nilo-Saharan languages on individual Chadic languages in the respective border areas? In this context, detailed comparative studies between Chadic and Adamawa on the one hand, Chadic and Jukunoid and Chadic and Jarawan Bantu on the other hand as well as Chadic internal research, are urgently required

Spatial evolution of human dialects

The geographical pattern of human dialects is a result of history. Here, we formulate a simple spatial model of language change which shows that the final result of this historical evolution may, to some extent, be predictable. The model shows that the boundaries of language dialect regions are controlled by a length minimizing effect analogous to surface tension, mediated by variations in population density which can induce curvature, and by the shape of coastline or similar borders. The predictability of dialect regions arises because these effects will drive many complex, randomized early states toward one of a smaller number of stable final configurations. The model is able to reproduce observations and predictions of dialectologists. These include dialect continua, isogloss bundling, fanning, the wave-like spread of dialect features from cities, and the impact of human movement on the number of dialects that an area can support. The model also provides an analytical form for S\'{e}guy's Curve giving the relationship between geographical and linguistic distance, and a generalisation of the curve to account for the presence of a population centre. A simple modification allows us to analytically characterize the variation of language use by age in an area undergoing linguistic change

Complex systems and the history of the English language

Complexity theory (Mitchell 2009, Kretzschmar 2009) is something that historical linguists not only can use but should use in order to improve the relationship between the speech we observe in historical settings and the generalizations we make from it. Complex systems, as described in physics, ecology, and many other sciences, are made up of massive numbers of components interacting with one another, and this results in self-organization and emergent order. For speech, the “components” of a complex system are all of the possible variant realizations of linguistic features as they are deployed by human agents, speakers and writers. The order that emerges in speech is simply the fact that our use of words and other linguistic features is significantly clustered in the spatial and social and textual groups in which we actually communicate. Order emerges from such systems by means of self-organization, but the order that arises from speech is not the same as what linguists study under the rubric of linguistic structure. In both texts and regional/social groups, the frequency distribution of features occurs as the same pattern: an asymptotic hyperbolic curve (or “A-curve”). Formal linguistic systems, grammars, are thus not the direct result of the complex system, and historical linguists must use complexity to mediate between the language production observed in the community and the grammars we describe. The history of the English language does not proceed as regularly as like clockwork, and an understanding of complex systems helps us to see why and how, and suggests what we can do about it. First, the scaling property of complex systems tells us that there are no representative speakers, and so our observation of any small group of speakers is unlikely to represent any group at a larger scale—and limited evidence is the necessary condition of many of our historical studies. The fact that underlying complex distributions follow the 80/20 rule, i.e. 80% of the word tokens in a data set will be instances of only 20% of the word types, while the other 80% of the word types will amount to only 20% of the tokens, gives us an effective tool for estimating the status of historical states of the language. Such a frequency-based technique is opposed to the typological “fit” technique that relies on a few texts that can be reliably located in space, and which may not account for the crosscutting effects of text type, another dimension in which the 80/20 rule applies. Besides issues of sampling, the frequency-based approach also affects how we can think about change. The A-curve immediately translates to the S-curve now used to describe linguistic change, and explains that “change” cannot reasonably be considered to be a qualitative shift. Instead, we can use to model of “punctuated equilibrium” from evolutionary biology (e.g., see Gould and Eldredge 1993), which suggests that multiple changes occur simultaneously and compete rather than the older idea of “phyletic gradualism” in evolution that corresponds to the traditional method of historical linguistics. The Great Vowel Shift, for example, is a useful overall generalization, but complex systems and punctuated equilibrium explain why we should not expect it ever to be “complete” or to appear in the same form in different places. These applications of complexity can help us to understand and interpret our existing studies better, and suggest how new studies in the history of the English language can be made more valid and reliable

Speech and language therapy versus placebo or no intervention for speech problems in Parkinson's disease

Parkinson's disease patients commonly suffer from speech and vocal problems including dysarthric speech, reduced loudness and loss of articulation. These symptoms increase in frequency and intensity with progression of the disease). Speech and language therapy (SLT) aims to improve the intelligibility of speech with behavioural treatment techniques or instrumental aids

Exemplar Dynamics Models of the Stability of Phonological Categories

We develop a model for the stability and maintenance of phonological categories. Examples of phonological categories are vowel sounds such as "i" and "e". We model such categories as consisting of collections of labeled exemplars that language users store in their memory. Each exemplar is a detailed memory of an instance of the linguistic entity in question. Starting from an exemplar-level model we derive integro-differential equations for the long-term evolution of the density of exemplars in different portions of phonetic space. Using these latter equations we investigate under what conditions two phonological categories merge or not. Our main conclusion is that for the preservation of distinct phonological categories, it is necessary that anomalous speech tokens of a given category are discarded, and not merely stored in memory as an exemplar of another category.Comment: 6 pages, COGS201

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State Vowel Identification

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. Such a transformation enables speech to be understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitchindependent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

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

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