1. Modelling the evolution of language for modellers and non-modellers EvoLang 2004 1 Vowel Systems Practical Example

2. Modelling the evolution of language for modellers and non-modellers EvoLang 2004 2 Why speech? Cross-linguistic data available –On universals –On acquisition –On language change This data is relatively uncontroversial –As opposed to e.g. syntax

3. Modelling the evolution of language for modellers and non-modellers EvoLang 2004 3 Speech is easy to model It is a physical signal We can use existing techniques –Speech synthesis techniques –Speech processing techniques –Even neural processing models Results are directly comparable to the real thing

4. Modelling the evolution of language for modellers and non-modellers EvoLang 2004 4 The aim of the study Explain universals of vowel systems –Why are do certain (combinations of) vowels occur more often than others (acoustic distinctiveness) –How does the optimisation take place? Hypothesis –Self-organisation in a population under constraints of production, perception, learning causes optimal systems to emerge Model –Agent-based model –Imitation games

5. Modelling the evolution of language for modellers and non-modellers EvoLang 2004 5 Computational considerations Simplification 1 –Agents communicate formants, not complete signals –Greatly reduces the number of computations –Perception, production already in terms of formants Simplification 2 –No meaning (problem: phonemes are defined in terms of meaning) –Imitation is used instead of distinguishing meaning

6. Modelling the evolution of language for modellers and non-modellers EvoLang 2004 6 Architecture For vowels: Realistic production articulatory synthesiser (Maeda, Valleé) Realistic perception Formant weighting (Mantakas, Schwarz, Boë) Learning model Prototype based associative memory Sounds ProductionPerception Associative Memory

7. Modelling the evolution of language for modellers and non-modellers EvoLang 2004 7 The interactions Imitation with categorical perception –Humans hear speech signals as the nearest phoneme in their language (?) Correctness of imitation depends not only on the signals used, but also on the agents’ repertoires Initiator Imitator

8. Modelling the evolution of language for modellers and non-modellers EvoLang 2004 8 Imitation failure Initiator Imitator

9. Modelling the evolution of language for modellers and non-modellers EvoLang 2004 9 Distributed probabilistic optimization Distributed probabilistic optimization Pick an agent from the population Pick a signal from this agent Modify the signal randomly Play imitation games with all other agents in the population If success of modification is higher than success of original vowel, keep the change, otherwise revert. Disadvantage: –Number of signals per agent is fixed beforehand

10. Modelling the evolution of language for modellers and non-modellers EvoLang 2004 10 Reactions to imitation game Merge Shift Closer F2F2 F1F1 Add Vowel Throw away Vowel

11. Modelling the evolution of language for modellers and non-modellers EvoLang 2004 11 Measures Imitative success Energy of vowel systems (Liljencrants & Lindblom) Size Preservation –Success of imitation between agents from populations a number of generations apart –Only in systems with changing populations Realism