How do speech patterns spread through a community?

1. How do speech patterns spread through a community? Or, “Oh no, not another linguistics model”

2. Purpose of Models • Suppose you have two populations: native English speakers and native German speakers • Speakers vary in how they produce certain sounds • If all speakers are part of same community what factors determine if a particular speech style will spread to everyone? • What style would it be?

3. Two related models • Spatial model - agents wander around • Network model - agents are connected in a network • When people interact with each other will depend on the model • How they interact will be the same in both models..

4. Rules for interaction - based on exemplar model • Idea of the sound of a word isn’t one ideal pronunciation • Instead, a word (or category) is represented as a list of possible instances of that word (exemplars) • [word1 word2 word3] word

5. Exemplar model of production and perception • Produce a word - select one of the exemplars of that category • [word1 word2 word3] • Perceive a word - try to match that exemplar to existing exemplars in your categories, then add it to the list • categoryA [word1 word2 word3] • categoryB [word2 word2 word2]

6. Speech production in this model • People know 6 word types • Short words that end in p,b,t,d,k, or g • Ex. “bat”, “mop” • Final consonant can be pronounced 3 different ways • 0 - unreleased • 1 - voiced & released • 2 - voiceless & released

7. Speech production in this model • Each of the 6 categories is made up of 10 exemplars • /__p/ [0 0 1 1 1 0 1 1 1 1] • To speak - choose random exemplar from the category • From /__p/ choose “0”

8. Speech perception in this model • To listen - try to match spoken exemplar to its corresponding category & its voiced or voiceless counterpart • Category pairs: p/b, t/d, k/g • /__p/ [0 0 1 1 1 0 1 1 1 1] • /__b/ [0 0 2 0 0 0 2 2 0 2] • Chance of assigning to a category based on square of # of matches • “0” has 3 matches for p, 6 matches for b • Four times as likely to be assigned to b • Spoken exemplar of “1” would always be assigned to p

9. Speech perception in this model • To assign a spoken exemplar to a category • Kick out a random exemplar • Replace with the spoken exemplar • /__b/ [0 0 2 0 0 0 2 2 0 2] • /__b/ [0 0 0 0 0 0 2 2 0 2] • If there were no matches, spoken exemplar does not get assigned to any category

10. Summary • Agents have 6 categories made up of 10 exemplars • Categories are initialized from an input text file, with data for 8 agents • But 8 isn’t very many agents, so you have the option of creating extras • Best way to see is to take a look at the model!