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Linking speech errors and phonological grammars: insights from Harmonic Grammar networks*

Published online by Cambridge University Press:  29 June 2009

Matthew Goldrick  and
Robert Daland
Matthew Goldrick
Affiliation:
Northwestern University
Robert Daland
Affiliation:
Northwestern University
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Abstract

Phonological grammars characterise distinctions between relatively well-formed (unmarked) and relatively ill-formed (marked) phonological structures. We review evidence that markedness influences speech-error probabilities. Specifically, although errors result in unmarked as well as marked structures, there is a markedness asymmetry: errors are more likely to produce unmarked outcomes. We show that stochastic disruption to the computational mechanisms realising a Harmonic Grammar (HG) can account for the broad empirical patterns of speech errors. We demonstrate that our proposal can account for the general markedness asymmetry. We also develop methods for linking particular HG proposals to speech-error distributions, and illustrate these methods using a simple HG and a set of initial consonant errors in English.

Type
Articles
Information
Phonology , Volume 26 , Issue 1 , May 2009 , pp. 147 - 185
Copyright
Copyright © Cambridge University Press 2009

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References

REFERENCES

Aarts, Emile H. L. & Korst, Jan (1989). Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing. Chichester: Wiley.Google Scholar
Abd-El-Jawad, Hassan & Abu-Salim, Issam (1987). Slips of the tongue in Arabic and their theoretical implications. Language Sciences 9. 145–171.Google Scholar
Anttila, Arto (1997). Deriving variation from grammar. In Hinskens, Frans, van Hout, Roeland & Leo Wetzels, W. (eds.) Variation, change and phonological theory. Amsterdam & Philadelphia: Benjamins. 35–68.CrossRefGoogle Scholar
Béland, Renée & Paradis, Carole (1997). Principled syllabic dissolution in a primary progressive aphasia case. Aphasiology 11. 1171–1196.CrossRefGoogle Scholar
Béland, Renée, Paradis, Carole & Bois, Monique (1993). Constraints and repairs in aphasic speech: a group study. Canadian Journal of Linguistics 38. 279–302.CrossRefGoogle Scholar
Berg, Thomas (1998). Linguistic structure and change: an explanation from language processing. Oxford: Clarendon.CrossRefGoogle Scholar
Bíró, Tamás S. (2006). Finding the right words: implementing Optimality Theory with simulated annealing. PhD dissertation, University of Groningen.Google Scholar
Blumstein, Sheila E. (1973). A phonological investigation of aphasic speech. The Hague: Mouton.Google Scholar
Boersma, Paul (1997). How we learn variation, optionality, and probability. Proceedings of the Institute of Phonetic Sciences of the University of Amsterdam 21. 43–58.Google Scholar
Boersma, Paul & Hayes, Bruce (2001). Empirical tests of the Gradual Learning Algorithm. LI 32. 45–86.Google Scholar
Boersma, Paul & Pater, Joe (2008). Convergence properties of a gradual learning algorithm for Harmonic Grammar. Ms, University of Amsterdam & University of Massachusetts, Amherst. Available as ROA-970 from the Rutgers Optimality Archive.Google Scholar
Buchwald, Adam B., Rapp, Brenda & Stone, Maureen (2007). Insertion of discrete phonological units: an articulatory and acoustic investigation of aphasic speech. Language and Cognitive Processes 22. 910–948.Google Scholar
Butterworth, Brian & Whittaker, Harry (1980). Peggy Babcock's relatives. In Stelmach, Georg E. & Requin, Jean (eds.) Tutorials in motor behavior. Amsterdam: North Holland. 647–656.Google Scholar
Chomsky, Noam (1980). Rules and representations. New York: Columbia University Press.CrossRefGoogle Scholar
Clements, G. N. (1990). The role of the sonority cycle in core syllabification. In Kingston, John & Beckman, Mary E. (eds.) Papers in laboratory phonology I: between the grammar and physics of speech. Cambridge: Cambridge University Press. 283–333.CrossRefGoogle Scholar
Coetzee, Andries W. (2006). Variation as accessing ‘non-optimal’ candidates. Phonology 23. 337–385.Google Scholar
Dell, Gary S. (1986). A spreading-activation theory of retrieval in sentence production. Psychological Review 93. 283–321.CrossRefGoogle ScholarPubMed
Dell, Gary S., Reed, Kristopher D., Adams, David R. & Meyer, Antje S. (2000). Speech errors, phonotactic constraints, and implicit learning: a study of the role of experience in language production. Journal of Experimental Psychology: Learning, Memory, and Cognition 26. 1355–1367.Google ScholarPubMed
Dell, Gary S., Schwartz, Myrna F., Saffran, Nadine Martin Eleanor M. & Gagnon, Deborah A. (1997). Lexical access in aphasic and nonaphasic speakers. Psychological Review 104. 801–838.Google Scholar
Frisch, Stefan (2000). Temporally organized lexical representations as phonological units. In Broe, Michael B. & Pierrehumbert, Janet B. (eds.) Papers in laboratory phonology V: acquisition and the lexicon. Cambridge: Cambridge University Press. 283–298.Google Scholar
Frisch, Stefan & Wright, Richard (2002). The phonetics of phonological speech errors: an acoustic analysis of slips of the tongue. JPh 30. 139–162.Google Scholar
Gamkrelidze, Thomas V. (1978). On the correlation of stops and fricatives in a phonological system. In Greenberg, Joseph H., Ferguson, Charles A. & Moravcsik, Edith A. (eds.) Universals of human language. Vol. 2: Phonology. Stanford: Stanford University Press. 9–46.Google Scholar
Goldrick, Matthew (2002). Patterns of sound, patterns in mind: phonological regularities in speech production. PhD dissertation, Johns Hopkins University.Google Scholar
Goldrick, Matthew (2004). Phonological features and phonotactic constraints in speech production. Journal of Memory and Language 51. 586–603.Google Scholar
Goldrick, Matthew (2007). Constraint interaction: a lingua franca for stochastic theories of language. In Schütze, Carson T. & Ferreira, Victor S. (eds.) The state of the art in speech error research: Proceedings of the LSA Institute Workshop. MIT Working Papers in Linguistics 53. 95–114.Google Scholar
Goldrick, Matthew & Blumstein, Sheila E. (2006). Cascading activation from phonological planning to articulatory processes: evidence from tongue twisters. Language and Cognitive Processes 21. 649–683.CrossRefGoogle Scholar
Goldrick, Matthew & Larson, Meredith (2008). Phonotactic probability influences speech production. Cognition 107. 1155–1164.Google Scholar
Goldrick, Matthew & Rapp, Brenda (2007). Lexical and post-lexical phonological representations in spoken production. Cognition 102. 219–260.Google Scholar
Goldstein, Louis, Pouplier, Marianne, Chen, Larissa, Saltzmann, Elliot & Byrd, Dani (2007). Dynamic action units slip in speech production errors. Cognition 103. 386–412.Google Scholar
Goldwater, Sharon & Johnson, Mark (2003). Learning OT constraint rankings using a Maximum Entropy model. In Spenador, Jennifer, Eriksson, Anders & Dahl, Östen (eds.) Proceedings of the Stockholm Workshop on Variation within Optimality Theory. Stockholm: Stockholm University. 111–120.Google Scholar
Greenberg, Joseph H. (1966). Language universals, with special reference to feature hierarchies. The Hague & Paris: Mouton.Google Scholar
Guy, Gregory & Boberg, Charles (1997). Inherent variability and the Obligatory Contour Principle. Language Variation and Change 9. 149–164.Google Scholar
Hayes, Bruce & Wilson, Colin (2008). A maximum entropy model of phonotactics and phonotactic learning. LI 39. 379–440.Google Scholar
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences 79. 2554–2558.CrossRefGoogle ScholarPubMed
Hopfield, J. J. (1984). Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences 81. 3088–3092.Google Scholar
Jäger, Gerhard (2007). Maximum entropy models and stochastic Optimality Theory. In Zaenen, Annie, Simpson, Jane, King, Tracy Holloway, Grimshaw, Jane, Maling, Joan & Manning, Chris (eds.) Architectures, rules, and preferences: variations on themes by Joan W. Bresnan. Stanford: CSLI. 467–479.Google Scholar
Jäger, Gerhard & Rosenbach, Anette (2006). The winner takes it all – almost: cumulativity in grammatical variation. Linguistics 44. 937–971.CrossRefGoogle Scholar
Jakobson, Roman (1941). Kindersprache, Aphasie, und allgemeine Lautgesetze. Translated 1968 by Allan R. Keiler as Child language, aphasia and phonological universals. The Hague & Paris: Mouton.Google Scholar
Jarosz, Gaja (2006). Rich lexicons and restrictive grammars: maximum likelihood learning in Optimality Theory. PhD dissertation, Johns Hopkins University.Google Scholar
Johnson, Mark (2002). Optimality-theoretic lexical functional grammar. In Merlo, Paola & Stevenson, Suzanne (eds.) The lexical basis of sentence processing: formal, computational and experimental issues. Amsterdam & Philadelphia: Benjamins. 59–74.Google Scholar
Kupin, Joseph J. (1982). Tongue twisters as a source of information about speech production. Bloomington: Indiana University Linguistics Club.Google Scholar
Laver, John (1980). Slips of the tongue as neuromuscular evidence for a model of speech production. In Dechert, Hans W. & Raupach, Manfred (eds.) Temporal variables in speech: studies in honour of Frieda Goldman-Eisler. The Hague: Mouton. 21–26.Google Scholar
Legendre, Géraldine, Miyata, Yoshiro & Smolensky, Paul (1990). Harmonic Grammar: a formal multi-level connectionist theory of linguistic well-formedness: theoretical foundations. In Proceedings of the 12th Annual Conference of the Cognitive Science Society. Hillsdale: Erlbaum. 388–395.Google Scholar
Legendre, Géraldine, Sorace, Antonella & Smolensky, Paul (2006). The Optimality Theory–Harmonic Grammar connection. In Smolensky, & Legendre, (2006b: vol. 2). 339–402.Google Scholar
Levitt, Andrea G. & Healy, Alice F. (1985). The roles of phoneme frequency, similarity, and availability in the experimental elicitation of speech errors. Journal of Memory and Language 24. 717–733.Google Scholar
Lombardi, Linda (1999). Positional faithfulness and voicing assimilation in Optimality Theory. NLLT 17. 267–302.Google Scholar
MacKay, Donald G. (1972). The structure of words and syllables: evidence from errors in speech. Cognitive Psychology 3. 210–227.Google Scholar
Maddieson, Ian (1984). Patterns of sounds. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Marshall, Jane (2006). Jargon aphasia: what have we learned? Aphasiology 20. 387–410.Google Scholar
McMillan, Corey T., Corley, Martin & Lickley, Robin J. (2009). Articulatory evidence for feedback and competition in speech production. Language and Cognitive Processes 24. 44–66.Google Scholar
Meyer, A. S. (1992). Investigation of phonological encoding through speech error analyses: achievements, limitations, and alternatives. Cognition 42. 181–211.Google Scholar
Moreton, Elliott (2004). Non-computable functions in Optimality Theory. In McCarthy, John J. (ed.) Optimality Theory in phonology: a reader. Malden, Mass.: Blackwell. 141–163.Google Scholar
Motley, Michael T. & Baars, Bernard J. (1975). Encoding sensitivities to phonological markedness and transitional probabilities: evidence from spoonerisms. Human Communication Research 1. 353–361.Google Scholar
Mowrey, Richard A. & MacKay, Ian R. A. (1990). Phonological primitives: electromyographic speech error evidence. JASA 88. 1299–1322.CrossRefGoogle ScholarPubMed
Ouden, Dirk-Bart den (2002). Phonology in aphasia: syllables and segments in level-specific deficits. PhD dissertation, University of Groningen.Google Scholar
Pater, Joe (to appear). Weighted constraints in generative linguistics. Cognitive Science.Google Scholar
Pierrehumbert, Janet B. (2002). Word-specific phonetics. In Gussenhoven, Carlos & Warner, Natasha (eds.) Laboratory Phonology 7. Berlin & New York: Mouton de Gruyter. 101–139.CrossRefGoogle Scholar
Potts, Chris, Pater, Joe, Bhatt, Rajesh & Becker, Michael (2008). Harmonic Grammar with Linear Programming: from linguistic systems to linguistic typology. Ms, University of Massachusetts, Amherst. Available as ROA-984 from the Rutgers Optimality Archive.Google Scholar
Pouplier, Marianne (2007). Tongue kinematics during utterances elicited with the SLIP technique. Language and Speech 50. 311–341.Google Scholar
Pouplier, Marianne (2008). The role of a coda consonant as error trigger in repetition tasks. JPh 36. 114–140.Google Scholar
Pouplier, Marianne & Hardcastle, William (2005). A re-evaluation of the nature of speech errors in normal and disordered speakers. Phonetica 62. 227–243.Google Scholar
Prince, Alan (2007). The pursuit of theory. In de Lacy, Paul (ed.) The Cambridge handbook of phonology. Cambridge: Cambridge University Press. 33–60.CrossRefGoogle Scholar
Prince, Alan & Smolensky, Paul (1993). Optimality Theory: constraint interaction in generative grammar. Ms, Rutgers University & University of Colorado, Boulder. Published 2004, Malden, Mass. & Oxford: Blackwell.Google Scholar
Rapp, Brenda & Goldrick, Matthew (2006). Speaking words: contributions of cognitive neuropsychological research. Cognitive Neuropsychology 23. 39–73.CrossRefGoogle ScholarPubMed
Reynolds, William (1994). Variation and phonological theory. PhD dissertation, University of Pennsylvania.Google Scholar
Roberts, Julie (1997). Acquisition of variable rules: a study of (-t, d) deletion in preschool children. Journal of Child Language 24. 351–372.CrossRefGoogle Scholar
Romani, Cristina & Calabrese, Andrea (1998). Syllabic constraints in the phonological errors of an aphasic patient. Brain and Language 64. 83–121.Google Scholar
Romani, Cristina & Galluzzi, Claudia (2005). Effects of syllabic complexity in predicting accuracy of repetition and direction of errors in patients with articulatory and phonological difficulties. Cognitive Neuropsychology 22. 817–850.Google Scholar
Romani, Cristina, Olson, Andrew, Semenza, Carlo & Granà, Alessia (2002). Patterns of phonological errors as a function of a phonological versus an articulatory locus of impairment. Cortex 38. 541–567.Google Scholar
Shattuck-Hufnagel, Stefanie & Klatt, Dennis H. (1979). The limited use of distinctive features and markedness in speech production: evidence from speech error data. Journal of Verbal Learning and Verbal Behavior 18. 41–55.Google Scholar
Smolensky, Paul (1986). Information processing in dynamical systems: foundations of Harmony Theory. In Rumelhart, D. E., MacClelland, J. L. & The PDP Research Group (eds.) Parallel Distributed Processing: explorations in the micro-structure of cognition. Vol. 1. Cambridge, Mass.: MIT Press. 194–281.Google Scholar
Smolensky, Paul (2006a). Optimization in neural networks: harmony maximization. In Smolensky, & Legendre, (2006b: vol. 1). 345–392.Google Scholar
Smolensky, Paul (2006b). Computational levels and integrated connectionist/ symbolic explanation. In Smolensky, & Legendre, (2006b: vol. 2). 503–592.Google Scholar
Smolensky, Paul & Legendre, Géraldine (2006a). Formalizing the principles II: optimization and grammar. In Smolensky, & Legendre, (2006b: vol. 1). 207–234.Google Scholar
Smolensky, Paul & Legendre, Géraldine (2006b). The harmonic mind: from neural computation to optimality-theoretic grammar. 2 vols. Cambridge, Mass.: MIT Press.Google Scholar
Smolensky, Paul, Legendre, Géraldine & Tesar, Bruce (2006). Optimality Theory: the structure, acquisition, and use of grammar. In Smolensky, & Legendre, (2006b: vol. 1). 453–544.Google Scholar
Soderstrom, Melanie, Mathis, Donald W. & Smolensky, Paul (2006). Abstract genomic encoding of universal grammar in Optimality Theory. In Smolensky, & Legendre, (2006b: vol. 2). 403–471.Google Scholar
Stemberger, Joseph Paul (1983). Speech errors and theoretical phonology: a review. Bloomington: Indiana University Linguistics Club.Google Scholar
Stemberger, Joseph Paul (2004). Neighbourhood effects on error rates in speech production. Brain and Language 90. 413–422.Google Scholar
Taylor, Conrad F. & Houghton, George (2005). Learning artificial phonotactic constraints: time course, durability, and relationship to natural constraints. Journal of Experimental Psychology: Learning, Memory, and Cognition 31. 1398–1416.Google Scholar
Tesar, Bruce (1999). Robust interpretive parsing in metrical stress theory. WCCFL 17. 625–639.Google Scholar
Vitevitch, Michael S. & Luce, Paul A. (1999). Probabilistic phonotactics and neighborhood activation in spoken word recognition. Journal of Memory and Language 40. 374–408.Google Scholar
Vousden, Janet I., Brown, Gordon D. A. & Harley, Trevor A. (2000). Serial control of phonology in speech production: a hierarchical model. Cognitive Psychology 41. 101–175.Google Scholar
Wan, I-Ping & Jaeger, Jeri (1998). Speech errors and the representation of tone in Mandarin Chinese. Phonology 15. 417–461.Google Scholar
Warker, Jill A. & Dell, Gary S. (2006). Speech errors reflect newly learned phonotactic constraints. Journal of Experimental Psychology: Learning, Memory, and Cognition 32. 387–398.Google Scholar
Warker, Jill A., Dell, Gary S., Whalen, Christine A. & Gereg, Samantha (2008). Limits on learning phonotactic constraints from recent production experience. Journal of Experimental Psychology: Learning, Memory, and Cognition 34. 1289–1295.Google ScholarPubMed
Wilson, Colin (2006). Learning phonology with substantive bias: an experimental and computational study of velar palatalization. Cognitive Science 30. 945–982.Google Scholar
Xu, Zong-Ben, Hu, Guo-Qing & Kwong, Chung-Ping (1996). Asymmetric Hopfield-type networks: theory and applications. Neural Networks 9. 483–501.Google Scholar
Zipf, George (1935). The psycho-biology of language: an introduction to dynamic philology. Boston: Houghton Mifflin.Google Scholar
Zwicky, Arnold M. (1980). ‘Internal’ and ‘external’ evidence in linguistics. In PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association. Vol. 2: Symposia and invited papers. Chicago: University of Chicago Press. 598–604.Google Scholar
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