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

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

Modelling the evolution of language for modellers and non-modellers EvoLang 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

Modelling the evolution of language for modellers and non-modellers EvoLang 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

Modelling the evolution of language for modellers and non-modellers EvoLang 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

Modelling the evolution of language for modellers and non-modellers EvoLang 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

Modelling the evolution of language for modellers and non-modellers EvoLang 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

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

Modelling the evolution of language for modellers and non-modellers EvoLang 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

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

Modelling the evolution of language for modellers and non-modellers EvoLang 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