The DOI matches its Crossref record; 35 of the 50 references it cites were found in a registry or an index.
Distributional regularity and phonotactic constraints are useful for segmentation
Open accessAbstract
In order to acquire a lexicon, young children must segment speech into words, even though most words are unfamiliar to them. This is a non-trivial task because speech lacks any acoustic analog of the blank spaces between printed words. Two sources of information that might be useful for this task are distributional regularity and phonotactic constraints. Informally, distributional regularity refers to the intuition that sound sequences that occur frequently and in a variety of contexts are better candidates for the lexicon than those that occur rarely or in few contexts. We express that intuition formally by a class of functions called DR functions. We then put forth three hypotheses: First, that children segment using DR functions. Second, that they exploit phonotactic constraints on the possible pronunciations of words in their language. Specifically, they exploit both the requirement that every word must have a vowel and the constraints that languages impose on word-initial and word-final consonant clusters. Third, that children learn which word-boundary clusters are permitted in their language by assuming that all permissible word-boundary clusters will eventually occur at utterance boundaries. Using computational simulation, we investigate the effectiveness of these strategies for segmenting broad phonetic transcripts of child-directed English. The results show that DR functions and phonotactic constraints can be used to significantly improve segmentation. Further, the contributions of DR functions and phonotactic constraints are largely independent, so using both yields better segmentation than using either one alone. Finally, learning the permissible word-boundary clusters from utterance boundaries does not degrade segmentation performance.
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“Across a language sample, sounds that co-occur within words tend to be more highly correlated with one another than sound pairs spanning word boundaries.”
From Incidental Language Learning: Listening (and Learning) Out of the Corner of Your Ear (1997) · cites this paper as Brent & Cartwright, 1996
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“infants may be able to exploit statistical cues to word boundaries”
From Incidental Language Learning: Listening (and Learning) Out of the Corner of Your Ear (1997) · cites this paper as Brent & Cartwright, 1996
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“recent computational models of language acquisition suggest that at least some pertinent language-learning mechanisms may induce structure from the input using statistical methods similar to those suggested by incidental-learning research”
From Incidental Language Learning: Listening (and Learning) Out of the Corner of Your Ear (1997) · cites this paper as Brent & Cartwright, 1996
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Citations
Cites
Harris, Z. (1954) · doi:10.1007/978-94-017-6059-1_36 · Cited by 5,146 · cited 2×
“Word-based Strategies Olivier (1968) proposed a procedure that maintains a lexicon and uses it for segmenting.”
MethodHarris, Z. S. (1954) · WORD · doi:10.1080/00437956.1954.11659520 · Cited by 4,807
The TRACE model of speech perception
McClelland, J. L., & Elman, J. L. (1986) · Cognitive Psychology · doi:10.1016/0010-0285(86)90015-0 · Cited by 3,044
Language learnability and language development
Pye, C., & Pinker, S. (1985) · Language · doi:10.2307/414499 · Cited by 2,400 · cited 1×
“At one extreme, the phonological component might segment perfectly, as assumed in most attempts to explain the syntactic and semantic components of lexical acquisition (e.g., Pinker, 1984; Siskind, this issue).”
BackgroundStochastic Complexity in Statistical Inquiry
Rissanen, J. (1989) · World Scientific Series in Computer Science · doi:10.1142/0822 · Cited by 1,840
Word Segmentation: The Role of Distributional Cues
Saffran, J. R., Newport, E. L., & Aslin, R. N. (1996) · Journal of Memory and Language · doi:10.1006/jmla.1996.0032 · Cited by 1,405
Shortlist: a connectionist model of continuous speech recognition
Norris, D. G. (1994) · Cognition · doi:10.1016/0010-0277(94)90043-4 · Cited by 1,287 · cited 1×
“It has been proposed that such regularities play a role in early word discovery (Cutler, 1994, 1996; Cutler, Mehler, Norris, & Segui, 1994).”
BackgroundPapers in Laboratory Phonology: The role of the sonority cycle in core syllabification
Clements, G. (1990) · Cambridge University Press eBooks · doi:10.1017/cbo9780511627736.017 · Cited by 1,157
Infants′ Detection of the Sound Patterns of Words in Fluent Speech
Jusczyk, P. W., & Aslin, R. N. (1995) · Cognitive Psychology · doi:10.1006/cogp.1995.1010 · Cited by 1,061 · cited 1×
“Jusczyk and Aslin (1995) familiarized 7.5-month-old American infants with two diierent monosyllabic words and then presented them with passages that either did or did not contain the familiar words in sentences.”
BackgroundInfluentialInfants' Preference for the Predominant Stress Patterns of English Words
Jusczyk, P. W., Cutler, A., & Redanz, N. J. (1993) · Child Development · doi:10.1111/j.1467-8624.1993.tb02935.x · Cited by 1,006 · cited 1×
“Jusczyk, Cutler, and Redanz (1993) have found that 9-month-old American infants listen longer to words with strong-weak stress (the predominant pattern in English) than to words with weak-strong stress, but 6-month-olds show no preference.”
Background
Research works printed but not linked: 3
- Bernstein-Ratner, 1987, The phonology of parent child speech, Vol. 6
- Ellison, T.M. (forthcoming). The machine learning of phonological structure. Cambridge, UK: Cambridge University Press.
- Newsome, 1995, Do infants use stress as a cue in segmenting fluent speech?, Vol. 2, 415
Cited by
Sorted by citations
Items 301–329 of 329
The intelligibility of consonants in American English infant-directed speech
Swingley, D. (2025) · Cognitive Psychology · doi:10.1016/j.cogpsych.2025.101766
Using Context to Improve Word Segmentation
Hu, S., & Guo, X. (2025) · arXiv.org · doi:10.48550/arxiv.2503.10023
“We obtained a corpus of 9790 lines of transcribed child-directed speech derived from the Bernstein-Ratner corpus in the CHILDES database; these utterances are specifically directed towards 13-to 23-month-olds [3, 5, 8].”
In defense of epicyclesPreprint
Endress, A. (2021) · Mind & Language · doi:10.31234/osf.io/bu4m5
Phonological constraints in speech segmentation processes: investigating levels of implementation
Crouzet, O., & Bacri, N. (1999) · EUROSPEECH · doi:10.21437/eurospeech.1999-673
“The first kind of model that could account for these effects is grounded on transitional probabilities ([2], [3], [9]) and would predict that high probability strings are likely to belong to the same word whereas low probability strings would rather span lexical boundaries.”
BackgroundGonzález, E. (2015) · Revista Española de Lingüística Aplicada/Spanish Journal of Applied Linguistics · doi:10.1075/resla.28.1.13var
Chapitre 8. Mémoire et apprentissage
Perruchet, P., & Poulin-Charronnat, B. (2012) · Éditions de la Maison des sciences de l’homme eBooks · doi:10.4000/books.editionsmsh.14817 · Cited by 1
Wu, Q.-Y., Fang, X., Chen, Q.-J., Li, Y.-R., & Deng, Y. (2011) · Lecture notes in electrical engineering · doi:10.1007/978-3-642-25553-3_25
“Previous studies have shown that adults as well as infants are capable of segmenting a continuous speech stream into words based on statistical cues derived from the distributional analysis of patterns of words [1-3].”
BackgroundJohnson, B., & Murchison, C. (2006) · The American Journal of Psychology · doi:10.2307/1415648
“…segmentation skills, in turn, make it possible for infants to track the relations (statistical and algebraic) among linguistically relevant units (Brent & Cartwright, 1996; Chambers, Onishi, & Fisher, 2003; Gomez & Gerken, 1999; Marcus, Vijayan, Rao, & Vishton, 1999; Mintz & Gleitman, 2002; Pena…”
BackgroundComputational Approaches to Language Acquisition
Elman, J. (2006) · Elsevier eBooks · doi:10.1016/b0-08-044854-2/00855-5
“For excellent discussion of related computational approaches, see Brent (1996), Munakata and McClelland (2003) and MacWhinney (1998, 1999).”
Explaining Neuropsychological Double Dissociations
Friedemann Pulvermüller, A. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615528.007
Regulation, Overlap, and Web Tails
Friedemann Pulvermüller, A. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615528.008
Synfire Chains as the Basis of Serial Order in the Brain
Friedemann Pulvermüller, A. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615528.011
Friedemann Pulvermüller, A. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615528.013
Multiple Reverberations and Multiple Center Embeddings
Friedemann Pulvermüller, A. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615528.019
Friedemann Pulvermüller, A. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615528.017
Neural Algorithms and Neural Networks
Friedemann Pulvermüller, A. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615528.009
Friedemann Pulvermüller, A. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615528.020
Friedemann Pulvermüller, A. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615528.003
Friedemann Pulvermüller, A. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615528.016
Neuronal Grammar and Algorithms
Friedemann Pulvermüller, A. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615528.014
Friedemann Pulvermüller, A. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615528.012
Multiple Reverberation for Resolving Lexical Ambiguity
Friedemann Pulvermüller, A. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615528.018
Friedemann Pulvermüller, A. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615528.021
Friedemann Pulvermüller, A. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615528.001
Paul (2007) · doi:10.1044/1092-4388(2007/094
Metadata
- Authors
- Brent, M. R., & Cartwright, T. A.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Brent, Michael R.
- ORCID
- Brent, Michael R.
- Venue
- Cognition
- Publisher
- Elsevier BV
- Published
- 1996-10
- Volume
- 61
- Issue
- 1-2
- Pages
- 93-125
- ISSN
- 0010-0277 (ISSN-L)
- OpenAlex
- W2029948425
- Cited by
- 514
- References
- 50
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 4 · 2023: 7 · 2022: 44 · 2021: 12 · 2020: 9 · 2019: 13 · 2018: 10 · 2017: 13 · 2016: 35 · 2015: 24 · 2014: 14 · 2013: 15 · 2012: 15
- FWCI
- 31.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cognition (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Phonetics and Phonology Research
- Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
- Keywords
- speech segmentation, phonotactic constraints, word learning, child language acquisition, word boundaries, consonant clusters, utterance boundaries, computer simulation
- MeSH terms
- Adult, Algorithms, Attention, Child, Child, Preschool, Female, Humans, Infant, Language Development, Male, Phonetics, Mental Recall, Verbal Learning, Vocabulary
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