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Vowel Systems

This study explores the evolution of vowel systems through agent-based modeling techniques, aimed at both modellers and non-modellers. It investigates the universals of vowel usage, examining why certain vowel combinations are more prevalent due to factors like acoustic distinctiveness. The model simplifies communication to formants, avoiding complex signals, and employs imitation games to simulate language evolution. Key aspects include the analysis of imitation success, vowel energy, and the dynamics of agent interactions, providing an innovative perspective on language acquisition and change.

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Vowel Systems

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

  6. For vowels: Realistic productionarticulatory synthesiser(Maeda, Valleé) Realistic perceptionFormant weighting(Mantakas, Schwarz, Boë) Learning modelPrototype based associative memory Associative Memory Perception Production Sounds Architecture 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 Modelling the evolution of language for modellers and non-modellers EvoLang 2004

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

  9. 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 2004

  10. Reactions to imitation game F2 Shift Closer F1 Throw away Vowel Add Vowel Merge 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 Modelling the evolution of language for modellers and non-modellers EvoLang 2004

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