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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 101–200 of 329
Zipfian frequency distributions facilitate word segmentation in context.
Kurumada, C., Meylan, S. C., & Frank, M. C. (2013) · Cognition · doi:10.1016/j.cognition.2013.02.002 · Cited by 77
English-learning infants’ segmentation of trisyllabic words from fluent speech
Houston, D., Santelmann, L., & Jusczyk, P. (2004) · Language and Cognitive Processes · doi:10.1080/01690960344000143 · Cited by 75
“Also, because the listener’s goal is to identify sequences of sounds in fluent speech that match stored representations, the mental lexicon itself may play an important role in segmentation (Brent, 1999; Brent & Cartwright, 1996; Suomi, 1993).”
Universality Versus Language-Specificity in Listening to Running Speech
Cutler, A., Demuth, K., & McQueen, J. (2002) · Psychology Science · doi:10.1111/1467-9280.00447 · Cited by 75
“Indeed, Brent and Cartwright (1996) suggested just such a constraint on initial word learning.”
BackgroundAdding Generalization to Statistical Learning: The Induction of Phonotactics from Continuous Speech.
Adriaans, F., & Kager, R. (2010) · Journal of Memory and Language · doi:10.1016/j.jml.2009.11.007 · Cited by 74
“Brent and Cartwright (1996) propose that phonotactic constraints can be learned through inspection of consonant clusters that appear at utterance boundaries.”
BackgroundMethodThe emergence of links between lexical acquisition and object categorization: a computational study
Chen, Y. (2005) · Connection science · doi:10.1080/09540090500281554 · Cited by 73
“Many existing models of language acquisition have been evaluated by artificially derived data of speech and semantics (Brent and Cartwright 1996, Siskind 1996, Bailey et al. 1998, Cohen et al. 2001).”
MethodFrom Universal Laws of Cognition to Specific Cognitive Models
Chater, N., & Brown, G. D. A. (2008) · Cognitive Science · doi:10.1080/03640210701801941 · Cited by 63
Language-universal constraints on speech segmentation
Norris, D., McQueen, J., Cutler, A., Butterfield, S., & Kearns, R. (2001) · doi:10.1080/01690960143000119 · Cited by 68
“If the PWC has the same form across languages, it could assist infants in their initial attempts to segment continuous speech and acquire their rst words (Johnson, Jusczyk, Cutler & Norris, 2000; cf. Brent & Cartwright, 1996).”
BackgroundThe acquisition of morphological knowledge investigated through artificial language learning
Merkx, M., Rastle, K., & Davis, M. H. (2011) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2010.538211 · Cited by 67
“…that this combinatorial property of affixes should provide for highly efficient segmentation through chunking as demonstrated in similar theories of speech segmentation (e.g., Brent, 1997; Brent & Cartwright, 1996; see also Dahan & Brent, 1999) and visual word recognition (Davis, 1999, 2010).”
MethodMENTOR: an enabler for interoperable intelligent systems
Sarraipa, J., Jardim-Gonçalves, R., & Steiger-Garção, A. (2010) · International Journal of General Systems · doi:10.1080/03081079.2010.484278 · Cited by 67
“For a human, knowing a language implies having a mental lexicon, i.e. a memorised set of associations among sound sequences, their meanings and their syntactic privileges (Brent and Cartwright 2002).”
Background‘Ideal learning’ of natural language: Positive results about learning from positive evidence
Chater, N., & Vitanyi, P. M. B. (2007) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2006.10.002 · Cited by 112
“Moreover, the Simplicity Principle has been used practically in a wide range of models of language processing and structure (e.g., Brent & Cartwright, 1996; Dowman, 2000; Ellison, 1992; Goldsmith, 2001; Onnis, Roberts & Chater, 2002; Wolff, 1988).”
How Infant Speech Perception Contributes to Language Acquisition
Gervain, J., & Werker, J. F. (2008) · Language and Linguistics Compass · doi:10.1111/j.1749-818x.2008.00089.x · Cited by 54
“Fourthly, it has long been recognized that the statistical regularities of phoneme co-occurrences are also reliable indicators of morpheme and word boundaries (Harris 1955; Brent and Cartwright 1996).”
BackgroundConstructing a Proto-Lexicon: An Integrative View of Infant Language Development
Johnson, E. K. (2015) · Annual Review of Linguistics · doi:10.1146/annurev-linguistics-011415-040616 · Cited by 119
“…lexical searches (e.g., Shukla et al. 2007, 2011), constraints on minimal word lengths such that all words contain at least one vowel (e.g., Brent & Cartwright 1996, Johnson et al. 2003), and possibly the Unique Stress Constraint (no word can contain more than one syllable with primary…”
BackgroundMethodRäsänen, O. J. (2012) · Speech Communication · doi:10.1016/j.specom.2012.05.001 · Cited by 61
Cortical responses to natural speech reflect probabilistic phonotactics
Liberto, G. M. D., Wong, D. D. E., Melnik-Leroy, G. A., & Alain De Cheveigné, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.037 · Cited by 57
Quantitative Linguistic Predictors of Infants’ Learning of Specific English Words
Swingley, D., & Humphrey, C. (2017) · Child Development · doi:10.1111/cdev.12731 · Cited by 102
“B&S further proposed that words discovered by being heard in isolation could then go on to assist in the discovery of other words using a kind of “remainder-is-a-new-potential-word” assumption (formalized in Brent & Cartwright, 1996).”
BackgroundThe use of sequential probabilities in the segmentation of speech
Lugt, A. V. (2001) · Perception & Psychophysics · doi:10.3758/bf03194440 · Cited by 59
“Several computational demonstrations of word segmentation in acquisition ( Brent & Cartwright, 1996; Cairns, Shillcock,Chater, & Levy, 1997; Christiansen, Allen, & Seidenberg, 1998) have also confirmed the utility of sequential probabilities as cues to the location of word boundaries.”
BackgroundRepetition-spacing and item-overlap effects in the Hebb repetition task
Page, M. P. A., Cumming, N., Norris, D., McNeil, A., & Hitch, G. (2013) · doi:10.1016/j.jml.2013.07.001 · Cited by 58
Chunking and data compression in verbal short-term memory
Norris, D. G., & Kalm, K. (2020) · Cognition · doi:10.1016/j.cognition.2020.104534 · Cited by 83
“For example, Brent and Cartwright (1996) proposed a model based on Minimal Description Length.”
MethodA joint model of word segmentation and meaning acquisition through cross-situational learning.
Räsänen, O., & Rasilo, H. (2015) · Psychology Review · doi:10.1037/a0039702 · Cited by 56
Modeling the contribution of phonotactic cues to the problem of word segmentation
Blanchard, D., Heinz, J. N., & Golinkoff, R. M. (2010) · Journal of Child Language · doi:10.1017/s030500090999050x · Cited by 51
“The BR corpus is the same one that Brent (1999), Venkataraman (2001), Goldwater (2007), Fleck (2008) and Johnson & Goldwater (2009) used to evaluate their models, and it has become the de facto standard for segmentation testing ever since it was phonemicized by Brent & Cartwright (1996).”
BackgroundMethodProbabilistic and distributional approaches to language acquisition.
Redington, M., & Chater, N. (1997) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(97)01081-4 · Cited by 56
Perruchet, P. (2018) · Topics in Cognitive Science · doi:10.1111/tops.12403 · Cited by 73
One model for the learning of language
Yang, Y., & Piantadosi, S. T. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2021865119 · Cited by 50
“This was necessary in order to avoid having to deal with hypotheses that generate infinite sequences (which are possible, although more challenging technically), and also may better match the observation that utterance boundaries provide an important cue in segmentation (136).”
Bootstrapping mechanisms in first language acquisition
Höhle, B. (2009) · Linguistics · doi:10.1515/ling.2009.013 · Cited by 64
Designing Speech for a Recipient
Fischer, K. (2016) · Pragmatics & beyond. New series · doi:10.1075/pbns.270 · Cited by 60
Spoken Word Recognition: A Stage-processing Approach to Language Differences
Kolinsky, R. (1998) · The European Journal of Cognitive Psychology · doi:10.1080/713752266 · Cited by 52
Understanding Child Language Acquisition
Rowland, C. (2013) · doi:10.4324/9780203776025 · Cited by 50
Silva, R. R., Chrobot, N., Newman, E. J., Schwarz, N., & Topolinski, S. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.02200 · Cited by 56
“…were construed so as to have a high to low vowel-consonant ratio and then arranging the letters into sequences that either did or did not conform to German phonotactic constraints (Brent and Cartwright, 1996; Saffran, 2003; Warker and Dell, 2006) according to the authors’ subjective judgment.”
BackgroundLudusan, B., Versteegh, M., Jansen, A., Gravier, G., Cao, X.-N., Johnson, M., & Dupoux, E. (2014) · International Conference on Language Resources and Evaluation · doi:10.63317/3qg6wsfaq9ut · Cited by 46
“Most of the systems from the NLP community take as input a speech corpus that has been transcribed phonemically (gold transcription), but where the word boundaries have been deleted (Brent and Cartwright, 1996; Brent, 1999; Johnson et al., 2007; Goldwater et al., 2009).”
BackgroundLexical, Syntactic, and Stress-Pattern Cues for Speech Segmentation
Sanders, L. D., & Neville, H. J. (2000) · Journal of Speech Language and Hearing Research · doi:10.1044/jslhr.4306.1301 · Cited by 40
Co-occurrence statistics as a language-dependent cue for speech segmentation.
Saksida, A., Langus, A., & Nespor, M. (2017) · Developmental Science · doi:10.1111/desc.12390 · Cited by 45
“Although smaller and larger constituents are sometimes considered as well (Batchelder, 2002; Bonatti, Pe~na, Nespor & Mehler, 2007; Brent & Cartwright, 1996; Monaghan et al., 2005), syllable has been predominantly recognized as a minimal perceptual unit in speech and has therefore been used as a…”
Lexical viability constraints on speech segmentation by infants.
Johnson, E. K., Jusczyk, P., Cutler, A., & Norris, D. (2003) · Cognitive Psychology · doi:10.1016/s0010-0285(02)00507-8 · Cited by 45
“Interestingly, the PWC is similar to a proposal made by Brent and Cartwright (1996) to improve performance of their computational model of vocabulary acquisition with a corpus of child-directed speech.”
BackgroundMethodOn Evaluation Metrics in Optimality Theory
Rasin, E., & Katzir, R. (2016) · Linguistic Inquiry · doi:10.1162/ling_a_00210 · Cited by 89
“…principle for the scientist or as a learning criterion for the learner—notably, Berwick (1982), Rissanen and Ristad (1994), Stolcke (1994), Brent and Cartwright (1996), Grünwald (1996), de Marcken (1996), Clark (2001), Goldsmith (2001), Dowman (2007), Hsu and Chater (2010), Hsu, Chater,…”
BackgroundMethodVisual speech segmentation: using facial cues to locate word boundaries in continuous speech
Mitchel, A. D., & Weiss, D. J. (2013) · Language and Cognitive Processes · doi:10.1080/01690965.2013.791703 · Cited by 44
“…task may involve numerous strategies incorporating acoustic (e.g. Christophe, Gout, Peperkamp, & Morgan, 2003; Jusczyk, Houston, & Newsome, 1999), phonotactic (e.g. Friederici & Wessels, 1993), distributional (e.g. Saffran, Newport, & Aslin, 1996) and pragmatic cues (e.g. Brent & Cartwright, 1996).”
Perruchet, P., & Poulin-Charronnat, B. (2012) · Journal of Memory and Language · doi:10.1016/j.jml.2012.02.010 · Cited by 44
“Instead of looking for units’ boundaries, the general strategy shared by all the chunk-based models (e.g., Brent & Cartwright, 1996; Frank, Goldwater, Griffiths, & Tenenbaum, 2010; Perruchet & Vinter, 1998; Robinet et al., 2011; Servan-Schreiber & Anderson, 1990) is that a large number of potential…”
BackgroundMethodToward a Unified Model of Lexical Acquisition and Lexical Access
Brent, M. (1997) · Journal of Psycholinguistic Research · doi:10.1023/a:1025032825951 · Cited by 43
“Likewise, it is clear that infants could use the phonotactic regularities of their languages, such as the fact that English words cannot begin with gd, to avoid erroneous segmentations such as bi gdog (e.g., Brent & Cartwright, 1996 ).”
BackgroundInfluentialSelf-citationConnectionist and Statistical Approaches to Language Acquisition: A Distributional Perspective
Redington, M., & Chater, N. (1998) · Language and Cognitive Processes · doi:10.1080/016909698386500 · Cited by 107
Finding Words in Speech: An Investigation of American English
Hockema, S. (2006) · doi:10.1207/s15473341lld0202_3 · Cited by 42
“…(e.g., Aslin et al., 1998; Gleitman & Rozin, 1977; Mehler, 1981; Savin & Bever, 1970; Swingley, 2005; Thiessen & Saffran, 2003), that phonemes are (Brent & Cartwright, 1996; Lamel & Zue, 1984; Mattys & Jusczyk, 2001; McQueen, 1998; Norris, McQueen, Cutler, & Butterfield, 1997; among many others),…”
MethodThe language machine: Psycholinguistics in review
Altmann, G. T. M. (2001) · British Journal of Psychology · doi:10.1348/000712601162130 · Cited by 91
Bootstrapping lexical acquisition: The role of prosodic structure
Christophe, A., & Dupoux, E. (1996) · The Linguistic Review · doi:10.1515/tlir.1996.13.3-4.383 · Cited by 42
Connecting Cues: Overlapping Regularities Support Cue Discovery in Infancy
Sahni, S., Seidenberg, M. S., & Saffran, J. R. (2010) · Child Development · doi:10.1111/j.1467-8624.2010.01430.x · Cited by 41
“…account, they detect the regular alternation and map it onto onsets, potentially because onsets are privileged perceptually and ⁄ or lexically (e.g., Brent & Cartwright, 1996; Jusczyk, Jusczyk, Kennedy , Schomberg, & Koenig, 1995; Magnuson, Dixon, Tanenhaus, & Aslin, 2007; Marslen-Wilson &…”
Perruchet, P., & Tillmann, B. (2010) · Cognitive Sciences · doi:10.1111/j.1551-6709.2009.01074.x · Cited by 41
Learning Nonadjacent Dependencies: No Need for Algebraic-Like Computations.
Perruchet, P., Tyler, M. D., Galland, N., & Peereman, R. (2004) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.133.4.573 · Cited by 97
“The question is, Why does Parser find out the remaining third of the words, instead of the part words, which have the same distributional properties? Parser exploits here a property that is also exploited in other nonconnectionist models of word segmentation, such as the Brent and Cartwright (1996) model.”
BackgroundRäsänen, O. J. (2011) · Cognition · doi:10.1016/j.cognition.2011.04.001 · Cited by 38
“…tested on phonetic transcription data include a connectionist model by Christiansen, Allen, and Seidenberg (1998), a further MDL-based algorithm by Brent and Cartwright (1996), the somewhat similar but incremental probabilistic approach known as the Model-based dynamic programming (MBDP-1)…”
MethodFernandes, T., Kolinsky, R., & Ventura, P. (2009) · Cognition · doi:10.1016/j.cognition.2009.05.002 · Cited by 40
Applications of corpus-based semantic similarity and word segmentation to database schema matching
Islam, A., Inkpen, D., & Kiringa, I. (2008) · The VLDB journal · doi:10.1007/s00778-007-0067-9 · Cited by 40
Statistical information and coarticulation as cues to word boundaries: A matter of signal quality
Fernandes, T., Ventura, P., & Kolinsky, R. (2007) · Perception & Psychophysics · doi:10.3758/bf03193922 · Cited by 40
“TP-words and part-words, we know that although this factor is potentially important to word discovery by infants ( Brent & Cartwright, 1996 ) and can explain participants’ performance in some studies (see, e.g., Dahan & Brent, 1999), it cannot always explain the impact of TPs (Aslin, Saffran, & Newport, 1998).”
BackgroundNarrowing the distance to language
Jusczyk, P. W. (1999) · Journal of Communication Disorders · doi:10.1016/s0021-9924(99)00014-3 · Cited by 35
“Other investigators have pointed to the potential utility of phonotactic cues in word segmentation (Brent & Cartwright, 1996; Cairns, Shillcock, Chater, & Levy, 1997).”
BackgroundSimplifying Reading: Applying the Simplicity Principle to Reading
Vousden, J. I., Ellefson, M. R., Solity, J. E., & Nick Chater, A. (2010) · Cognitive Science · doi:10.1111/j.1551-6709.2010.01134.x · Cited by 75
Local Redundancy Governs Infants' Spontaneous Orienting to Visual-Temporal Sequences
Addyman, C., & Denis Mareschal, B. (2013) · Child Development · doi:10.1111/cdev.12060 · Cited by 29
“(2002) and Bulf et al. (2011). This study shows that there need not have been any statistical learning in those studies.”
BackgroundLanguage Learning From Positive Evidence, Reconsidered: A Simplicity‐Based Approach
Hsu, A., Nick Chater, A., & Vitanyi, P. M. B. (2013) · Topics in Cognitive Science · doi:10.1111/tops.12005 · Cited by 30
“…and cognition (e.g., Attneave & Frost, 1969; Jacob Feldman, 2000; Hochberg & McAlister, 1953; Leeuwenberg, 1969), including language (e.g., Brent & Cartwright, 1996; Dowman, 2000; Ellison, 1992; Goldsmith, 2001; Onnis, Roberts & Chater, 2002; Vousden, Ellefson, Solity & Chater, 2011;…”
Jusczyk, P. (1997) · Current Directions in Psychological Science · doi:10.1111/1467-8721.ep10772947 · Cited by 39
MDLChunker: A MDL‐Based Cognitive Model of Inductive Learning
Robinet, V., Lemaire, B., & Gordon, M. B. (2011) · Cognitive Science · doi:10.1111/j.1551-6709.2011.01188.x · Cited by 38
“Brent & Cartwright (1996) used it in order to account for the ability of children to segment a stream of phonemes into words.”
MethodMemory-Prediction Errors and Their Consequences in Schizophrenia
Kraus, M., Keefe, R. S. E., & Krishnan, R. K. R. (2009) · Neuropsychology Review · doi:10.1007/s11065-009-9106-1 · Cited by 35
“Speech consists of a largely contiguous stream of auditory signals; the demarcations between the beginning and ending of words are frequently ambiguous in a purely acoustic analysis (Brent and Cartwright 1996; Christiansen et al. 1998; Klatt 1980).”
BackgroundReduplicated Words Are Easier to Learn
Ota, M., & Skarabela, B. (2016) · Language Learning and Development · doi:10.1080/15475441.2016.1165100 · Cited by 38
“…corpus is smaller than the Brent corpus that was used for the analysis of reduplicated words presented earlier, but it was selected here because of the availability of a phonologically represented derived corpus prepared by Brent and Cartwright (1996) and contributed to CHILDES by Sharon Goldwater.”
MethodTopolinski, S., Bakhtiari, G., & Erle, T. M. (2015) · Cognition · doi:10.1016/j.cognition.2015.10.019 · Cited by 33
TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning
Denis Mareschal, B., & French, R. M. (2016) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2016.0057 · Cited by 44
“Finally, the model was able to scale up to more realistic linguistic corpora , in particular, the Brent & Cartwright [21] data.”
When forgetting fosters learning: A neural network model for Statistical Learning
Endress, A., & Johnson, S. P. (2021) · Cognition · doi:10.1016/j.cognition.2021.104621 · Cited by 36
WSDL term tokenization methods for IR-style Web services discovery
Wu, C. (2012) · Science of Computer Programming · doi:10.1016/j.scico.2011.08.001 · Cited by 35
Priors in Bayesian Learning of Phonological Rules
Goldwater, S., & Johnson, M. (2004) · SIGMORPHON@ACL · doi:10.3115/1622153.1622158 · Cited by 35
“MDL-based systems have been relatively successful for tasks including word segmentation (de Marcken, 1996; Brent and Cartwright, 1996), morphological Figure 1: An example signature segmentation (Goldsmith, 2001; Creutz and Lagus, 2002), discovery of syllabicity and sonority (Elli-son, 1993), and…”
MethodAn Efficient Algorithm for Unsupervised Word Segmentation with Branching Entropy and MDL
Zhikov, V., Takamura, H., & Okumura, M. (2010) · Conference on Empirical Methods in Natural Language Processing · doi:10.1527/tjsai.28.347 · Cited by 34
Diminutives in child-directed speech supplement metric with distributional word segmentation cues
Kempe, V., Brooks, P. J., & Gillis, S. (2005) · Psychonomic Bulletin & Review · doi:10.3758/bf03196360 · Cited by 34
“Computational studies have confirmed that CDS constitutes input for word segmentation models that is superior to that of ADS (Aslin, Woodward, LaMendola, & Bever, 1996; Batchelder, 2002; Brent & Cartwright, 1996; Christiansen, Allen, & Seidenberg, 1998).”
The Recognition of Spoken Words
Frauenfelder, U. H., & Floccia, C. (1999) · Springer eBooks · doi:10.1007/978-3-642-59967-5_1 · Cited by 34
The statistical signature of morphosyntax: A study of Hungarian and Italian infant-directed speech
Gervain, J., & Erra, R. G. (2012) · Cognition · doi:10.1016/j.cognition.2012.06.010 · Cited by 33
Speech Perception and Spoken Word Recognition: Research and Theory
Cleary, M., & Pisoni, D. B. (2008) · doi:10.1002/9780470753477.ch16 · Cited by 45
“Early investigators, including Broadbent (1955), reported that listeners perceived a single “fused” syllable when a synthetic first formant was presented to one ear and a synthetic second formant (or second and third formant) to the other.”
BackgroundToni Cunillera, B., Càmara, E., Laine, M., & Rodríguez‐Fornells, A. (2009) · Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) · doi:10.1027/1618-3169/a000017 · Cited by 30
“…between statistical learning and lexical knowledge in speech segmentation is lacking, there is at least one computational model directly relevant to this issue, namely the INCDROP (INCremental Distributional Regularity OPtimization; Brent & Cartwright, 1996; Brent, 1997; Dahan & Brent, 1999).”
BackgroundKolodny, O., Lotem, A., & Edelman, S. (2015) · Cognitive Sciences · doi:10.1111/cogs.12140 · Cited by 32
Phillips, L., & Pearl, L. (2015) · Cognitive Science · doi:10.1111/cogs.12217 · Cited by 36
On the unsupervised analysis of domain-specific Chinese texts
Deng, K., Bol, P., Li, K. J., & Liu, J. S. (2016) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1516510113 · Cited by 31
Universals of listening: Equivalent prosodic entrainment in tone and non-tone languages.
Ip, M., & Cutler, A. (2020) · Cognition · doi:10.1016/j.cognition.2020.104311 · Cited by 30
New evidence for chunk-based models in word segmentation.
Perruchet, P., Poulin-Charronnat, B., Tillmann, B., & Peereman, R. (2014) · Acta Psychologica · doi:10.1016/j.actpsy.2014.01.015 · Cited by 29
Event-related potentials index segmentation of nonsense sounds
Sanders, L. D., Ameral, V. E., & Sayles, K. (2008) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2008.11.005 · Cited by 29
“Instead, listeners were likely using multiple acoustic segmentation cues including allophonic variation, phonotactic constraints, and language-specific rhythmic properties (Brent & Cartwright, 1996; Cutler, Mehler, Norris, & Sequi, 1992; Jusczyk, 1999).”
Linguistic Evolution Through Language Acquisition: List of contributors
Briscoe, E. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511486524 · Cited by 29
“…has been used to develop learnability proofs for non-finite classes of grammar (e.g. Muggleton, 1996) and to develop theoretical and computational models of lexical and grammatical acquisition (e.g. Brent and Cartwright, 1996; de Marcken, 1996; Rissanen and Ristad, 1994; Osborne and Briscoe, 1997).”
MethodLearning to Discover, Ground and Use Words with Segmental Neural Language Models
Kawakami, K., Dyer, C., & Blunsom, P. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1645 · Cited by 23
“Machine learning has contributed much to this discussion by showing that predictive models of language are capable of inferring the existence of word boundaries solely based on statistical properties of the input (Elman, 1990; Brent and Cartwright, 1996; Goldwater et al., 2009).”
BackgroundWords, rules, and mechanisms of language acquisition.
Endress, A., & Bonatti, L. (2016) · Wiley Interdisciplinary Reviews: Cognitive Science · doi:10.1002/wcs.1376 · Cited by 28
Word Segmentation Cues in German Child-Directed Speech: A Corpus Analysis
Stärk, K., Kidd, E., & Frost, R. (2021) · Language and Speech · doi:10.1177/0023830920979016 · Cited by 27
“All of the cues we examined have the potential to aid word segmentation, and of course, might boost the effect when infants can draw on a combination of cues, as is the case in natural language (Brent & Cartwright, 1996; Matzinger et al., 2019).”
BackgroundWhy Talk with Children Matters: Clinical Implications of Infant- and Child-Directed Speech Research
Ratner, N. (2013) · Seminars in Speech and Language · doi:10.1055/s-0033-1353449 · Cited by 27
“The prevalence of short utterances, repetition, and paraphrase in IDS/CDS is one reason why computer simulations of language learning do very well when provided with IDS/CDS corpora to “learn” from.(19,20) Speech to children differs in the types of utterances that are most prevalent, as well.”
BackgroundOn Leveraged Learning in Lexical Acquisition and Its Relationship to Acceleration
Mitchell, C., & McMurray, B. (2009) · Cognitive Sciences · doi:10.1111/j.1551-6709.2009.01071.x · Cited by 27
“Peters (1983) as well as Brent and Cartwright (1996) posit that children segment words from running speech using known words (or sequences of words) as access points. For example, in the phrase cognitive wugness, knowledge of the word cognitive can be used to segment and identify the word, wugness. In this way, each t…”
BackgroundInfluentialWordSeg: Standardizing unsupervised word form segmentation from text
Bernard, M., Thiollière, R., Saksida, A., Loukatou, G. R., Larsen, E., Johnson, M., Fibla, L., Emmanuel Dupoux, A., Daland, R., Cao, X., et al. (2019) · Behavior Research Methods · doi:10.3758/s13428-019-01223-3 · Cited by 35
Cascading and Multisensory Influences on Speech Perception Development
Choi, D., Black, A. K., & Werker, J. F. (2018) · Mind Brain and Education · doi:10.1111/mbe.12162 · Cited by 40
Defying the stimulus: acquisition of complex onsets in Polish*
Jarosz, G. (2017) · Phonology · doi:10.1017/s0952675717000148 · Cited by 25
Learning and Learnability in Phonology
Albright, A., & Hayes, B. (2011) · doi:10.1002/9781444343069.ch20 · Cited by 24
Perruchet, P. (2005) · Psychological Research · doi:10.1007/s00426-004-0205-6 · Cited by 24
“Let us illustrate the point using the model of word segmentation developed by Brent and Cartwright (1996), in which segmentation is construed as an optimization problem.”
MethodInfluentialConstraining the search for structure in the input
Jusczyk, P. W. (1998) · Lingua · doi:10.1016/s0024-3841(98)00034-5 · Cited by 20
Pauses enhance chunk recognition in song element strings by zebra finches
Spierings, M., de Weger, A., & ten Cate, C. (2015) · Animal Cognition · doi:10.1007/s10071-015-0855-3 · Cited by 23
“Computational models support the hypothesis that transitional information can be sufficient for correct word segmentation (8, 9).”
BackgroundDistribution-driven morpheme discovery: a computational/experimental study
Baroni, M. (2003) · Yearbook of morphology · doi:10.1007/978-1-4020-1513-7_9 · Cited by 23
“For example, even if it has been shown that distributional cues can be very effective for segmenting utterances into words (Brent and Cartwright 1996), there is no clear equivalent to semantically opaque morphemes in the domain of syntactic segmentation.”
BackgroundMethodInfluentialThe role of rhythmic cues for speech segmentation in monolingual and bilingual listeners
Goetry, V., & Kolinsky, R. (2000) · doi:10.5334/pb.960 · Cited by 23
Infants' developing sensitivity to native language phonotactics: A meta-analysis
Sundara, M., Zhou, Z. L., Breiss, C., Katsuda, H., & Steffman, J. (2021) · Cognition · doi:10.1016/j.cognition.2021.104993 · Cited by 21
Input frequency and the acquisition of syllable structure in Polish
Jarosz, G., Calamaro, S., & Zentz, J. (2017) · doi:10.1080/10489223.2016.1179743 · Cited by 22
Transitional probabilities count more than frequency, but might not be used for memorization
Endress, A. D., & Langus, A. (2016) · Cognitive Psychology · doi:10.1016/j.cogpsych.2016.11.004 · Cited by 33
“…a variety of guises might be used for extracting re-curring sequences has been implemented in numerous computational models (e.g., Batchelder, 2002; Brent & Cartwright, 1996; Christiansen, Allen, & Seidenberg, 1998; Frank, Goldwater, Griffiths, & Tenenbaum, 2010; Orb´an, Fiser, Aslin, & Lengyel,…”
Sohail, J., & Johnson, E. K. (2016) · Language Learning and Development · doi:10.1080/15475441.2015.1073153 · Cited by 25
“Indeed, the importance of UBs in word segmentation is supported by several computational models of early word segmentation (e.g., Aslin et al., 1996; Brent & Cartwright, 1996; Monaghan & Christiansen, 2010).”
MethodOrdina, M., & Nesporb, M. (2013) · Language Learning · doi:10.1111/lang.12024 · Cited by 22
Diminutives facilitate word segmentation in natural speech: Cross-linguistic evidence
Kempe, V., Brooks, P. J., Gillis, S., & Samson, G. (2007) · Memory & Cognition · doi:10.3758/bf03193313 · Cited by 21
“LaMendola & Bever, 1996; Brent & Cartwright, 1996; Christiansen, Allen & Seidenberg,”
BackgroundDP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon
Algayres, R., Ricoul, T., Karadayi, J., Laurençon, H., Zaiem, S., Mohamed, A., Sagot, B., & Emmanuel Dupoux, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00505 · Cited by 17
“To do so, they must solve a word segmentation problem, since words are rarely uttered in isolation but come up in multi-word utterances (Brent and Cartwright, 1996).”
BackgroundA cognitively plausible model for grammar induction
Katzir, R. (2015) · Journal of Language Modelling · doi:10.15398/jlm.v2i2.85 · Cited by 19
Introduction: concepts of development, learning, and acquisition
Lindner, K., & Hohenberger, A. (2009) · Linguistics · doi:10.1515/ling.2009.009 · Cited by 20
“…to the discovery of prosodic regularities (e.g., Jusczyk et al. 1993a, 1993b, 1994), to word segmentation with or without prosodic cues (e.g., Brent and Cartwright 1996; Christiansen et al. 1998; Sa¤ran et al. 1996a; Aslin et al. 1998; for adults Sa¤ran et al. 1996b), to word sequencing and…”
BackgroundBehme, C., & Deacon, S. H. (2008) · Philosophical Psychology · doi:10.1080/09515080802412321 · Cited by 18
“In section 4 we explore research showing that young infants can track statistical regularities of language (Brent & Cartwright, 1996; Mattys & Jusczyk, 2001; Maye et al., 2003; Saffran, 2001, 2002, 2003; Saffran, Aslin, & Newport, 1996).”
BackgroundBootstrapping language: Are infant statisticians up to the job?
Johnson, E. K. (2012) · De Gruyter eBooks · doi:10.1515/9781934078242.55 · Cited by 18
“Brent and Cartwright (1996) improved the performance of their distributional model for word segmentation by requiring that all possible parses contain a vowel.”
BackgroundSchmidt, J. R. (2021) · L’Année psychologique · doi:10.3917/anpsy1.212.0077 · Cited by 24
Trecca, F., McCauley, S. M., Andersen, S. R., Bleses, D., Basbøll, H., Højen, A., Madsen, T. O., Ribu, I., & Christiansen, M. H. (2018) · Language Learning · doi:10.1111/lang.12325 · Cited by 17
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review