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Vowel Systems

Vowel Systems. Practical Example. Why speech?. Cross-linguistic data available On universals On acquisition On language change This data is relatively uncontroversial As opposed to e.g. syntax. Speech is easy to model. It is a physical signal We can use existing techniques

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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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