Metathesis in English and Hebrew A Computational Account of Usage-Based Phonology

1. Paul De PalmaGeorge Luger Departments of Computer Science Gonzaga University University of New Mexico (depalma@gonzaga.edu) Metathesis in English and HebrewA Computational Account of Usage-Based Phonology

2. Metathesis • Reversal of the expected linear ordering of sounds • Instead of xy we find yx • Examples • tl shift: borrowed noun chipotle chipolte (SAE: a spice) • ts shift: binyan 5hitsader  histader(Modern Hebrew: “he got organized” ) • hr shift: dative singular tehernek  dative plural terhek (Hungarian: “load”) • rh shift: Expected tiirhisaskhus  actual tihriasku (Pawnee: “he is called”) • Metathesis Myth: sporadic, irregular, due to performance errors • String of sounds realized as xy in language A can be yx in language B

3. Model Levels • A usage-based phonological account (Elizabeth Hume)  primarily synchronic • Can be extended to language change Utterance Selection Theory (William Croft) • Genetic Algorithm operationalizes Utterance Selection Theory

4. A Usage-Based Account • Metathesis requires two conditions • An indeterminate speech signal • Output that conforms to existing patterns in language • Example: chipotle/chipolte • In SAE, tl (stop consonant preceding a lateral) is indeterminate • Stop consonant following the lateral is frequent in post-vocalic position (cold,sold,mold,fold,molt,bolt,jolt,colt) • SAE speakers transform tl to lt

5. Utterance Selection Theory • Natural Selection requires: • A population of individuals with distinct characteristics • A mechanism for replicating those characteristics • Interaction among individuals and the environment • Selective pressure from the environment producing differential reproduction of the individuals and characteristics • Extended to language: • Language: A population of utterances (not a system of signs or a collection of words and rules that operate on them) • Normal replication: utterance conforms to the conventions of language use • Altered replication: utterance violates convention • Selection: graduate establishment of a new convention through use

6. Genetic Algorithm (GA) • Operationalizes (i.e, renders computationally precise) • Usage-based account of metathesis • Usage-based account of language change • Based loosely on the Darwinian notion of natural selection

7. More Precisely GA() { Initialize(population); //build initial population ComputeCost(population); //apply cost function Sort(population); //rank population while (population has not converged on a good-enough solution) { Pair(population); //decide which members reproduce Mate(population); //exchange characteristics Mutate(population); //randomly perturb genes Sort(population); //rank population TestConvergence(population); //has a new species appeared? } }

8. Cost Function Embodies most of the theory being modeled. For example, • Prevocalic stop (e.g., te) is more salient than a postvocalic stop. Give a fitness boost. • Penalize words with postvocalic stops (e.g., et) • Glottals (e.g., g), liquids (e.g., l), glides (e.g., w) bleed into adjacent sounds when followed by a stop (e.g., t). Penalize sequences like lt. • A stop followed by any non-stop consonant (e.g., tl) is perceptually weak. Penalize stop/non-stop consonant sequences • A stop followed by a strident (e.g., ts) is perceptually weak. Penalize prestrident stops.

9. As A Result • Each utterance in the population is tagged with a collection of boosts and penalties • The collection makes the underlying phonological theory computationally precise

10. Method • Encode the GA as a collection of objects in Java executable under Linux • Parameters • Population size: 64 strings • Mutation factor: .5% • For each of 1, 2, 4 base strings in the population, begin at parity then double the number of target strings three times • Fill out the balance of the population with randomly generated character sequences • For each population configuration • Run GA 250 times • 250 generations per run • Collect results per run

11. More Precisely • Input an initial population of the base word and the target word • Generate random sequences of characters that fill out the population. • Assign a fitness value to each of the sequences that comprise the population. • Sort the population by fitness value • Collect the population into two-tuples from highest to lowest fitness • Exchange pieces of sounds between each pair • Randomly shift a fixed fraction of the sounds  the action of chemical/biological/radiological mutagens on individuals. • Sort the population. Stop if some predetermined condition is met, else go to step 3.

12. Results • chipotle/chipolte • After 60 generations chipolte tokens are 95% of the population • chipotle disappears within 3 generations • hitsader/histader • After 48 generations histader tokens are 97.3% of the population • hitsader disappears within 2 generations

13. Metathesis: Conclusions • Accurate but underspecified • Computational model supplies missing precision • Usage-based aspect modeled as a frequency affect • Target tokens tends stabilize more quickly at a higher fraction as their number in the initial population increases • The larger the number of base tokens in the initial population, the better the performance

14. Utterance Selection: Conclusions • Hume’s account of metathesis can be reframed as an account of (one type) of language change • Can be rendered computationally precise using the Genetic Algorithm

15. Base:Target Influences Stabilization

16. Data: Chipotle

17. Data: Hitsader

18. Future Research • Use transcribed corpora to determine the frequency of both vulnerable cues and the targets of metathetic change • Use frequencies to weight penalties and rewards (adding precision to statement like, “[they] contribute to indeterminacy: /t/ with perceptually vulnerable cues and /l/ with stretched out features,” Hume, 2004, p.223) • Generate all instances of metathesis within a language

19. References Croft, W. (2000). Explaining Language Change: An Evolutionary Approach. Harlow, England: Pearson. Hume, E. (2004). The Indeterminancy/Attestation Model of Metathesis. Language 80(2): 203-237.