Hostname: page-component-76d6cb85b7-dqfph Total loading time: 0 Render date: 2026-07-16T01:35:36.281Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling the contribution of phonotactic cues to the problem of word segmentation*

Published online by Cambridge University Press:  22 March 2010

DANIEL BLANCHARD ,
JEFFREY HEINZ  and
ROBERTA GOLINKOFF
DANIEL BLANCHARD*
Affiliation:
University of Delaware
JEFFREY HEINZ
Affiliation:
University of Delaware
ROBERTA GOLINKOFF
Affiliation:
University of Delaware
*
Address for correspondence: Daniel Blanchard, University of Delaware – Computer & Information Sciences, 101 Smith Hall, Newark, Delaware 19716, United States. e-mail: dsblanch@udel.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

How do infants find the words in the speech stream? Computational models help us understand this feat by revealing the advantages and disadvantages of different strategies that infants might use. Here, we outline a computational model of word segmentation that aims both to incorporate cues proposed by language acquisition researchers and to establish the contributions different cues can make to word segmentation. We present experimental results from modified versions of Venkataraman's (2001) segmentation model that examine the utility of: (1) language-universal phonotactic cues; (2) language-specific phonotactic cues which must be learned while segmenting utterances; and (3) their combination. We show that the language-specific cue improves segmentation performance overall, but the language-universal phonotactic cue does not, and that their combination results in the most improvement. Not only does this suggest that language-specific constraints can be learned simultaneously with speech segmentation, but it is also consistent with experimental research that shows that there are multiple phonotactic cues helpful to segmentation (e.g. Mattys, Jusczyk, Luce & Morgan, 1999; Mattys & Jusczyk, 2001). This result also compares favorably to other segmentation models (e.g. Brent, 1999; Fleck, 2008; Goldwater, 2007; Johnson & Goldwater, 2009; Venkataraman, 2001) and has implications for how infants learn to segment.

Information

Type
Articles
Information
Journal of Child Language , Volume 37 , Special Issue 3: Computational models of child language learning , June 2010 , pp. 487 - 511
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

[*]

This work was supported by a University of Delaware Research Foundation grant to the second author, and by NIH (5R01HD050199) and NSF grants (BCS-0642529) to the third author. We thank Vijay Shanker for valuable discussions, and Regine Lai and Aimee Stahl for feedback on the manuscript.

References

REFERENCES

Bernstein-Ratner, N. (1987). The phonology of parent–child speech. In Nelson, K. & van Kleeck, A. (eds), Children's language, Volume 6, 159–74. Hillsdale, NJ: Erlbaum.Google Scholar
Blanchard, D. & Heinz, J. (2008). Improving word segmentation by simultaneously learning phonotactics. In 12th Conference on Computational Natural Language Learning, 65–72. Morristown, NJ: Association for Computational Linguistics.Google Scholar
Bortfeld, H., Morgan, J., Golinkoff, R. & Rathbun, K. (2005). Mommy and me: Familiar names help launch babies into speech-stream segmentation. Psychological Science 16, 298–304.Google Scholar
Brent, M. R. (1999). An efficient, probabilistically sound algorithm for segmentation and word discovery. Machine Learning 34, 71–105.CrossRefGoogle Scholar
Brent, M. R. & Cartwright, T. (1996). Distributional regularity and phonotactic constraints are useful for segmentation. Cognition 61, 93–125.CrossRefGoogle ScholarPubMed
Brent, M. R. & Siskind, J. (2001). The role of exposure to isolated words in early vocabulary development. Cognition 81, B33–B44.Google Scholar
Chomsky, N. & Halle, M. (1965). Some controversial questions in phonological theory. Journal of Linguistics 1, 97–138.Google Scholar
Cole, R. & Jakimik, J. (1980). A model of speech perception. In Cole, R. (ed.), Perception and production of fluent speech, 136–63. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Coleman, J. & Pierrehumbert, J. (1997). Stochastic phonological grammars and acceptability. In Proceedings of the Third Meeting of the Association for Computational Linguistics SIGPHON, 49–56. Somerset, NJ: Association for Computational Linguistics.Google Scholar
Cutler, A. & Carter, D. (1987). The predominance of strong initial syllables in the English vocabulary. Computer Speech and Language 2, 133–42.CrossRefGoogle Scholar
Demuth, K. (1992). Acquisition of Sesotho. In Slobin, D. (ed.), The cross-linguistic study of language acquisition, Volume 3, 557–638. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Dixon, R. M. W. & Aikhenvald, A. Y. (2002). Word: A typological framework. In Dixon, R. M. W. & Aikhenvald, A. Y. (eds), Word: A cross-linguistic typology, 1–41. Cambridge: Cambridge University Press.Google Scholar
Fleck, M. M. (2008). Lexicalized phonotactic word segmentation. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, 130–38. Morristown, NJ: Association for Computational Linguistics.Google Scholar
Friederici, A. & Wessels, J. (1993). Phonotactic knowledge of word boundaries and its use in infant speech perception. Perception and Psychophysics 54, 287–95.CrossRefGoogle ScholarPubMed
Goldwater, S. (2007). Nonparametric Bayesian models of lexical acquisition. Unpublished doctoral dissertation, Brown University, Department of Cognitive and Linguistic Sciences.Google Scholar
Golinkoff, R. & Hirsh-Pasek, K. (2006). Baby wordsmith: From associationist to social sophisticate. Current Directions in Psychological Science 15, 30–33.CrossRefGoogle Scholar
Halle, M. (1978). Knowledge unlearned and untaught: What speakers know about the sounds of their language. In Halle, M., Bresnan, J. & Miller, G. A. (eds), Linguistic theory and psychological reality, 294–303. Cambridge, MA: MIT Press.Google Scholar
Harris, Z. (1954). Distributional structure. Word 10, 146–62.Google Scholar
Hayes, B. & Wilson, C. (2008). A maximum entropy model of phonotactics and phonotactic learning. Linguistic Inquiry 67, 379–440.CrossRefGoogle Scholar
Heinz, J. (2007). Inductive learning of phonotactic patterns. Unpublished doctoral dissertation, University of California, Los Angeles, Department of Linguistics.Google Scholar
Hollich, G., Hirsh-Pasek, K., Golinkoff, R., Brand, R. J., Brown, E., Chung, H. L., et al. (2000). Breaking the language barrier: An emergentist coalition model for the origins of word learning. Monographs of the Society for Research in Child Development 65, i–vi, 1–123.CrossRefGoogle ScholarPubMed
Johnson, M. (2008). Unsupervised word segmentation for Sesotho using adaptor grammars. In Proceedings of the Tenth Meeting of the Association for Computational Linguistics, SIGMORPHON, 20–27. Morristown, NJ: Association for Computational Linguistics.Google Scholar
Johnson, M. & Goldwater, S. (2009). Improving nonparameteric Bayesian inference: Experiments on unsupervised word segmentation with adaptor grammars. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 317–25. Morristown, NJ: Association for Computational Linguistics.Google Scholar
Jurafsky, D. & Martin, J. (2008). Speech and language processing, 2nd edn.Upper Saddle River, NJ: Prentice-Hall.Google Scholar
Jusczyk, P. (1993). From general to language specific capacities: The WRAPSA model of how speech perception develops. Journal of Phonetics 21, 3–28.Google Scholar
Jusczyk, P., Friederici, A., Wessels, J., Svenkerud, V. Y. & Jusczyk, A. M. (1993). Infants' sensitivity to the sound patterns of native language words. Journal of Memory and Language 32, 402–420.CrossRefGoogle Scholar
Jusczyk, P., Hohne, E. & Baumann, A. (1999). Infants' sensitivity to allophonic cues for word segmentation. Perception and Psychophysics 61, 1465–76.CrossRefGoogle ScholarPubMed
Jusczyk, P., Houston, D. & Newsome, M. (1999). The beginnings of word segmentation in English-learning infants. Cognitive Psychology 39, 159–207.CrossRefGoogle ScholarPubMed
MacWhinney, B. & Snow, C. (1985). The child language data exchange system. Journal of Child Language 12, 271–95.Google Scholar
Matthews, P. (1991). Morphology, 2nd edn.Cambridge: Cambridge University Press.Google Scholar
Mattys, S. & Jusczyk, P. (2001). Phonotactic cues for segmentation of fluent speech by infants. Cognition 78, 91–121.CrossRefGoogle ScholarPubMed
Mattys, S., Jusczyk, P., Luce, P. & Morgan, J. (1999). Phonotactic and prosodic effects on word segmentation in infants. Cognitive Psychology 38, 465–94.CrossRefGoogle ScholarPubMed
Mohri, M. (2005). Statistical natural language processing. In Lothaire, M. (ed.), Applied combinatorics on words, 210–40. Cambridge: Cambridge University Press.Google Scholar
Nelson, D. K., Jusczyk, P., Mandel, D., Myers, J., Turk, A. & Gerken, L. (1995). The head-turn preference procedure for testing auditory perception. Infant Behavior and Development 18, 111–16.Google Scholar
Saffran, J., Aslin, R. & Newport, E. (1996). Statistical learning by 8-month-old infants. Science 274, 1926–28.CrossRefGoogle ScholarPubMed
Saffran, J., Werker, J. & Werner, L. (2006). The infant's auditory world: Hearing, speech, and the beginnings of language. In Siegler, R. & Kuhn, D. (eds), 6th edition of the handbook of child development, Volume 2, 58–108. New York: Wiley.Google Scholar
Sapir, E. (1925). Sound patterns in language. Language 1, 37–51.Google Scholar
Shi, R. & Lepage, M. (2008). The effect of functional morphemes on word segmentation in preverbal infants. Developmental Science 11, 407–413.CrossRefGoogle ScholarPubMed
Teahan, W. J., McNab, R., Wen, Y. & Witten, I. H. (2000). A compression-based algorithm for Chinese word segmentation. Computational Linguistics 26(3), 375–93.Google Scholar
Thiessen, E. & Saffran, J. (2003). When cues collide: Use of stress and statistical cues to word boundaries by 7- to 9-month-old infants. Developmental Psychology 39, 706–716.CrossRefGoogle ScholarPubMed
Thiessen, E. & Saffran, J. (2007). Learning to learn: Infants' acquisition of stress-based strategies for word segmentation. Language Learning and Development 3, 73–100.Google Scholar
Toft, Z. (2002). The phonetics and phonology of some syllabic consonants in Southern British English. In Toft, Z. (ed.), Papers on phonetics and phonology: The articulation, acoustics and perception of consonants, Volume 28, 111–144.Google Scholar
Toro, J. M., Nespor, M., Mehler, J. & Bonatti, L. L. (2008). Finding words and rules in a speech stream: Functional differences between vowels and consonants. Psychological Science 19, 137–144.CrossRefGoogle Scholar
Venkataraman, A. (2001). A statistical model for word discovery in transcribed speech. Computational Linguistics 27, 352–72.CrossRefGoogle Scholar
Xie, Z. & Niyogi, P. (2006). Robust acoustic-based syllable detection. In INTERSPEECH-2006, paper 1327-Wed1BuP.6. Accessed at www.isca-speech.org/archive/interspeech_2006/CrossRefGoogle Scholar