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Vowel Systems Practical Example Modelling the evolution of language for modellers and non-modellers EvoLang 2004 1 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 2 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 3 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 4 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 5 Architecture For vowels: • Realistic production articulatory synthesiser (Maeda, Valleé) • Realistic perception Formant weighting (Mantakas, Schwarz, Boë) Associative Memory Perception Production • Learning model Prototype based associative memory Sounds Modelling the evolution of language for modellers and non-modellers EvoLang 2004 6 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 7 Imitation failure Initiator Imitator Modelling the evolution of language for modellers and non-modellers EvoLang 2004 8 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 9 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 10 Random Energy Distribution Random Success Distribution 250 Measures Optimal Energy Distribution 100 700 90 600 80 500 150 100 Frequency 70 Frequency Frequency 200 60 50 40 30 50 • Imitative success 100 10 0. 44 0. 46 0. 48 0. 50 0. 51 0. 53 0. 55 0. 57 0. 58 0. 60 0. 62 0 Energy 1. 48 1. 55 1. 63 1. 70 1. 77 1. 84 1. 92 1. 99 2. 06 2. 13 2. 21 0 7. 9 17 1 .8 40 0 .0 90 9 . 20 28 3. 4 32 1. 5 7. 03 8 8 2. E+ 32 03 5. E+ 23 03 1. E+ 18 03 2. E+ 65 04 E+ 04 300 200 20 0 400 Success Energy • Energy of vowel systems (Liljencrants & Lindblom) Success Distribution Size Distribution Frequency • Size 300 250 200 150 100 50 • Preservation 0. 87 0. 88 0. 89 0. 91 0. 92 0. 93 0. 94 0. 96 0. 97 0. 98 1. 00 0 180 160 140 120 100 80 60 40 20 0 2. 00 2. 29 2. 58 2. 87 3. 16 3. 45 3. 74 4. 03 4. 32 4. 61 4. 90 Frequency 350 500 450 400 350 300 250 200 150 100 50 0 Frequency 400 Success 0. 11 0. 39 0. 67 0. 96 1. 24 1. 52 1. 80 2. 09 2. 37 2. 65 2. 93 450 Energy Distribution Size Energy – Success of imitation between agents from populations a number of generations apart – Only in systems with changing populations 88 % 1 2 5 8 3 4 5 6 2 3 6 7 4% 1 F1 (Bark) 4 F1 (Bark) 3 F1 (Bark) • Realism 2 8% 1 8 13 12 11 F2' (Bark) 10 7 8 Modelling the evolution of language non-modellers 16 15 for 14 modellers 13 12 11 10 and 9 8 16 15 14 F2' (Bark) EvoLang 2004 5 6 7 4 9 8 16 15 14 13 12 11 F2' (Bark) 119 10 8