Historical sound change has its origins in the different ways in which sounds are transmitted between a speaker and hearer in everyday conversation. But under what conditions can such variation lead to sound change? This question is part of the actuation of sound change, recognised as one of the greatest challenges in linguistics, and which is about the conditions under which sound change takes place, and why languages can follow such different paths of sound change. The actuation puzzle remains unsolved principally because the beginning of sound change is so gradual that it is undetectable even with modern instrumentation. The project remedies this deficiency by recasting the elusive actuation puzzle through analyses of two dialects, A and B of the same language, such that sound change is more advanced in B than in A. To achieve generalisation, the dialect pairs are chosen from Bantu, Indo-European, and Japanese languages that differ markedly in their sound patterns and sociocultural background. The degree of A→B mapping is determined by the application of speech processing techniques combined with agent-based modelling in which real speakers from the dialects are represented by computational agents that communicate with each other. The combination of these analyses of real and simulated speech data are used to model the relationship between speech communication and sound change as a complex system in which speech sounds that are copied from each other by individuals in everyday conversation sporadically give rise to a shift in the pronunciation pattern of a dialect that affects the entire community.
The primary objective is to explain why sound change should happen under one set of circumstances but not another i.e. to account for the set of factors that separates language stability from language change. The associated secondary objectives are (1) to understand how very slight and often random variation in the speech signals transmitted between speakers and hearers can cause a grammatical, categorical sound change in the sound pattern of the dialect (2) to determine whether such a grammatical change is precipitated by certain types of social network structures that bind individuals in a community and (3) to make use of the cognitively inspired, agent-based, computational, model of speech to test what types of mapping between words, their pronunciation patterns, and the physical speech signals are most likely to lead to sound change.
