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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
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“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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BackgroundResultEpenthetic vowels in Japanese: A perceptual illusion?
Emmanuel Dupoux, A., Kakehi, K., Hirose, Y., Pallier, C., & Mehler, J. (1999) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/0096-1523.25.6.1568 · Cited by 606
The role of exposure to isolated words in early vocabulary development
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BackgroundSelf-citationDistributional Information: A Powerful Cue for Acquiring Syntactic Categories
Redington, M., Chater, N., & Finch, S. (1998) · Cognitive Science · doi:10.1207/s15516709cog2204_2 · Cited by 535
“The first, derived from signal detection theory, is becoming standard in this area (Brent & Cartwright, 1997; Christiansen, Allen & Seidenberg, in press).”
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Phonotactic and Prosodic Effects on Word Segmentation in Infants
Mattys, S. L., Jusczyk, P. W., Luce, P. A., & Morgan, J. L. (1999) · Cognitive Psychology · doi:10.1006/cogp.1999.0721 · Cited by 466
“Among the potential cues to word boundaries are (1) allophonic variations (Hohne & Jusczyk, 1994; Jusczyk, Hohne, & Bauman, in press-a), (2) distributional regularities (Brent & Cartwright, 1996; Goodsitt, Morgan, & Kuhl, 1993; Saffran, Aslin, & Newport, 1996), (3) prosody (Echols, Crowhurst, & Childers, 1997; Jusczyk…”
BackgroundResultInfluentialA Bayesian framework for word segmentation: Exploring the effects of context
Goldwater, S. J., Griffiths, T. L., & Johnson, M. (2009) · Cognition · doi:10.1016/j.cognition.2009.03.008 · Cited by 446
“…algorithm is intended to provide a more realistic simulation of human word segmentation than earlier batch learning algorithms (de Marcken, 1995; Brent and Cartwright, 1996), which assume that the entire corpus of data is available to the learner at once (i.e., the learner may iterate over the…”
InfluentialMorphological decomposition based on the analysis of orthography
Rastle, K., & Davis, M. H. (2008) · Language and Cognitive Processes · doi:10.1080/01690960802069730 · Cited by 384
“A similar statistical approach has been proposed by Brent and colleagues in word and morpheme discovery (Brent, 1993; Brent & Cartwright, 1996), though these implementations require a perhaps implausibly large memory for unanalysed sequences.”
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“However, none of these cues appear to be sufficient (singly, or in combination) to identify all of the critical boundaries in connected speech (Brent and Cartwright, 1996; Christiansen et al., 1998).”
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Chater, N., & Manning, C. D. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.006 · Cited by 411
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“However, as Brent and Cartwright (1996) pointed out, semantic categorization is a substantial problem in its own right and does not seem a robust foundation for phonological development.”
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Mattys, S., & Jusczyk, P. (2001) · Cognition · doi:10.1016/s0010-0277(00)00109-8 · Cited by 386
“The hypothesis tested in Experiment 2 is that a biphone with low within-word probability and high between-word probability is an efficient word onset cue ( Brent & Cartwright, 1996; Cairns, Shillcock, Chater, & Levy, 1997; Gaygen & Luce, submitted; Mattys et al., 1999).”
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“…of speech and language can provide a strong foundation for binding input into perceptually coherent and meaningful units like words (Figure 4C; Brent and Cartwright, 1996; McQueen, 1998; Saffran et al., 1996), which may not be as readily identifiable using amplitude-based temporal landmarks…”
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“While we and a number of other investigators (e.g. Brent & Cartwright, 1996;”
ResultAn Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery
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Self-citationThe possible-word constraint in the segmentation of continuous speech.
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MethodAn alternative view of the mental lexicon
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Learning words from sights and sounds: A computational model
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The Search for Simplicity: A Fundamental Cognitive Principle?
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“Computer simulations have shown that this strategy works well on a variety of real-life corpora, using a variety of algorithms (Batchelder, 2002; Brent, 1999; Brent & Cartwright , 1996; Cairns, Shillcock, Chater, & Levy, 1997; Christiansen, Allen, & Seidenberg, 1998; Venkataraman, 2001).”
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The Use of Predictive Dependencies in Language Learning
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“…models of corpora and speakers, it has been found that “distributional regularity and phonotactic constraints are useful for seg-mentation” (Brent and Cartwright, 1996). de Mar-cken (1996) proposed to deconstruct text recursively from sentences over words and morphs into characters through…”
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Constraints in Phonological Acquisition: Learning phonotactic distributions
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“…statistical sponges comes largely from studies focusing on, e.g. the discovery of word boundaries (Saffran, Newport, & Aslin, 1996), phonotactic constraints (Brent & Cartwright, 1996), and the assignment of words to grammatical categories (Mintz, Newport, & Bever, 1995; cf. Brill & Marcus, 1992).”
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Swingley, D. (2009) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2009.0107 · Cited by 222
A Probabilistic Computational Model of Cross‐Situational Word Learning
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An interaction between prosody and statistics in the segmentation of fluent speech.
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“How do infants learn to segment fluent speech into a series of words? Statistical strategies were proposed for the segmentation of words, based on distributional properties over sub-lexical units like phonemes or syllables (e.g., Harris, 1955; Brent & Cartwright, 1996; Dahan & Brent, 1999; Batchelder, 2002; Swingley,…”
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“…word-like units on the basis of the transitional probabilities between consecutive syllables (Saffran, Aslin, & Newport, 1996; though see Perruchet & Vinter, 1998, for an alternative interpretation of these results, and Brent & Cartwright, 1996; Dahan & Brent, 1999, for an alternative model).”
BackgroundLearning to Segment Speech Using Multiple Cues: A Connectionist Model
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“Brent and Cartwright (1996) suggest leaving such words out of a (training) corpus because they occur in isolation more often than ordinary words, their use is highly idiosyncratic, and there is no standardised orthographic spelling for many of them.”
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“Brent and colleagues (Brent & Cartwright, 1996; Cartwright & Brent, 1994) demonstrate the utility of supplying a model of segmentation with categorical phonotactic information consisting of all the legal initial and final consonant clusters found in the English lexicon.”
BackgroundThe Role of Embodied Intention in Early Lexical Acquisition
Chen, Y., Ballard, D., & Aslin, R. (2005) · Cognitive Sciences · doi:10.1207/s15516709cog0000_40 · Cited by 182
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“…researchers have countered that the linguistic input to children does contain sufficient information to account for their linguistic representations (Brent & Cartwright, 1996; Seidenberg, 1997), and moreover, that this information can be extracted by general purpose learning mechanisms (Elman,…”
BackgroundUnsupervised query segmentation using generative language models and wikipedia
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A brain perspective on language mechanisms: from discrete neuronal ensembles to serial order
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“The recurring sound sequences constituting words can be distinguished, on statistical grounds, from the more accidental sound sequences across word boundaries (Brent and Cartwright, 1996; Harris, 1955; Redlich, 1993).”
BackgroundStatistical clustering and the contents of the infant vocabulary
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“We will begin with the DR model of Brent and Cartwright (1996) .”
InfluentialSpeech segmentation and word discovery: a computational perspective
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“We used the MDL framework because it has been successfully applied to other language learning problems (e.g., Ellison, in press; Stolcke and Omohundro, 1994) and to modeling other aspects of language acquisition (Brent and Cartwright, 1996).”
MethodInfluentialSelf-citationConnectionist models of cognitive development: where next?
Elman, J. L. (2005) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.01.005 · Cited by 173
Do infants segment words or recurring contiguous patterns?
Mattys, S., & Jusczyk, P. (2001) · Journal of Experimental Psychology: Human Perception and Performance · doi:10.1037/0096-1523.27.3.644 · Cited by 162
Phonetic Detail in the Developing Lexicon
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A multimodal learning interface for grounding spoken language in sensory perceptions
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“…grammar performs unsupervised word segmentation with an 87% token f-score on the standard Brent version of the Bernstein-Ratner corpus (Bernstein-Ratner, 1987; Brent and Cartwright, 1996), which is an error reduction of over 35% compared to the best previously reported results on this corpus.”
MethodResultInfluentialLearning the unlearnable: the role of missing evidence.
Regier, T., & Gahl, S. (2004) · Cognition · doi:10.1016/j.cognition.2003.12.003 · Cited by 154
“Recently, a number of computational learning models have pursued probabilistic approaches to language learning (e.g. Brent & Cartwright, 1996; Eisner, 2002; Niyogi & Berwick, 1996).”
From Speech Perception to Morphology: Affix Ordering Revisited
Hay, J. (2002) · Language · doi:10.1353/lan.2002.0159 · Cited by 154
The secret is in the sound: from unsegmented speech to lexical categories
Christiansen, M. H., Onnis, L., & Hockema, S. A. (2009) · Developmental Science · doi:10.1111/j.1467-7687.2009.00824.x · Cited by 43
“…a conservative measure of word segmentation in which a word is considered to be correctly segmented only if a lexical boundary is predicted at the beginning and at the end of that word without any boundaries being predicted word-internally (Brent & Cartwright, 1996; Christiansen et al. , 1998).”
On-line Assessment of Statistical Learning by Event-related Potentials
Abla, D., Katahira, K., & Okanoya, K. (2008) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2008.20058 · Cited by 150
“Several behavioral experiments have reported evidence that infants and adults readily learn statistically defined patterns in auditory input sequences (Pena, Bonatti, Nespor, & Mehler, 2002; Seidenberg, MacDonald, & Saffran, 2002; Brent, 1999; Saffran, Johnson, Aslin, & Newport, 1999; Aslin, Saffran, & Newport, 1998;…”
Speech Perception and Spoken Word Recognition: Past and Present
Jusczyk, P. W., & Luce, P. A. (2002) · Ear and Hearing · doi:10.1097/00003446-200202000-00002 · Cited by 132
“Thus, as noted above, phonotactic cues (Brent & Cartwright, 1996; Cairns, Shillcock, Chater, & Levy, 1997) and prosodic stress cues (Cutler, 1990; 1994; Cutler & Norris, 1988) have been promoted as useful for segmenting English words.”
BackgroundA Statistical Model for Word Discovery in Transcribed Speech
Venkataraman, A. (2001) · International Conference on Computational Logic · doi:10.1162/089120101317066113 · Cited by 138
“…First, it demonstrates the applicability and competitiveness of a conservative traditional approach for a task for which nontraditional approaches have been proposed even recently (Brent, 1999; Brent and Cartwright, 1996; de Marcken, 1995; Elman, 1990; Christiansen, Allen, and Seidenberg, 1998).”
BackgroundMethodPhonology impacts segmentation in online speech processing
Onnis, L., Monaghan, P., Richmond, K., & Chater, N. (2005) · Journal of Memory and Language · doi:10.1016/j.jml.2005.02.011 · Cited by 135
“…and its relation to the phonological structure of the native language of the participants, provides a potentially rich source of information that learners may draw upon in performing experimental tasks (Brent & Cartwright, 1996; Friederici & Wessels, 1993; Mattys, J uscz yk, Luce, & Morgan, 1999).”
Reflections on Phonological Bootstrapping: Its Role for Lexical and Syntactic Acquisition
Christophe, A. (1997) · Language and Cognitive Processes · doi:10.1080/016909697386637 · Cited by 124
“As formulated by Brent and Cartwright (1996), “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 and in few contexts”.”
BackgroundInfluentialPerception of Prosodic Boundary Correlates by Newborn Infants
Christophe, A., Mehler, J., & Sebastián‐Gallés, N. (2001) · Infancy · doi:10.1207/s15327078in0203_6 · Cited by 126
TRACX: A recognition-based connectionist framework for sequence segmentation and chunk extraction.
French, R. M., Addyman, C., & Denis Mareschal, B. (2011) · Psychological Review · doi:10.1037/a0025255 · Cited by 120
Building phonotactic knowledge in bilinguals: Role of early exposure.
Sebastián‐Gallés, N., & Bosch, L. (2002) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/0096-1523.28.4.974 · Cited by 111
Unsupervised Language Acquisition: Theory and PracticePreprint
Clark, A. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0212024 · Cited by 79
“Brent and coworkers have produced work on segmentation (Brent & Cartwright, 1997), acquisition of syntactic categories (Brent, 1997), unsupervised learning of morphology (Snover & Brent, 2001) and learning of lexical syntax (Brent, 1991, 1993).”
BackgroundMethodTillmann, B., & McAdams, S. E. (2004) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.30.5.1131 · Cited by 131
Meaning and the brain: The neurosemantics of referential, interactive, and combinatorial knowledge
Friedemann Pulvermüller, A. (2011) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2011.03.004 · Cited by 140
Dominey, P. F., & Ramus, F. (2000) · Language and Cognitive Processes · doi:10.1080/016909600386129 · Cited by 97
“Peter Ford Dominey”
BackgroundInfluentialAmbiguity, Competition, and Blending in Spoken Word Recognition
Gaskell, M. G., & Marslen‐Wilson, W. D. (1999) · Cognitive Science · doi:10.1016/s0364-0213(99)00011-7 · Cited by 135
“Recent experimental and computational research has demonstrated the value of statistical and distributional information , in both the development of language abilities, and the adult system (Brent & Cartwright, 1996; Cairns, Shillcock, Chater & Levy, 1997; Saffran, Aslin & Newport, 1996).”
BackgroundIsolated words enhance statistical language learning in infancy
Lew‐Williams, C., Pelucchi, B., & Saffran, J. R. (2011) · Developmental Science · doi:10.1111/j.1467-7687.2011.01079.x · Cited by 115
“…2005; Johnson & Jusczyk, 2001), phrase-level prosody (Shukla, Nespor & Mehler, 2007), sentence position (Seidl & Johnson, 2006), language-specific phonotactic constraints (Brent & Cartwright, 1996), and concurrently presented visual information (Cunillera, Cµmara, Laine & Rodríguez-Fornells, 2010).”
BackgroundEnglish-learning Infants' Segmentation of Verbs from Fluent Speech
Nazzi, T., Dilley, L. C., Jusczyk, A. M., Shattuck-Hufnagel, S., & Jusczyk, P. (2005) · Language and Speech · doi:10.1177/00238309050480030201 · Cited by 107
The exploitation of distributional information in syllable processing
Perruchet, P., & Peereman, R. (2003) · Journal of Neurolinguistics · doi:10.1016/s0911-6044(03)00059-9 · Cited by 104
“As in Brent and Cartwright ( 1996 ) model , unit extraction depends on the overall consistency of the segmentation , because the units , which stands for attentional chunks , are discrete and disjunctive .”
MethodComprehension without segmentation: a proof of concept with naive discriminative learning
Baayen, R. H., Shaoul, C., Willits, J. A., & Ramscar, M. (2015) · Language Cognition and Neuroscience · doi:10.1080/23273798.2015.1065336 · Cited by 102
“Our approach differs from current approaches in machine learning (see, e.g., Brent and Cartwright, 1996; Brent, 1999; Johnson, 2008; Goldwater et al., 2009; Robinet et al., 2011; Synnaeve et al., 2014) in two important ways.”
MethodBootstrapping the lexicon: A computational model of infant speech segmentation
Batchelder, E. O. (2002) · Cognition · doi:10.1016/s0010-0277(02)00002-1 · Cited by 82
Memory-Based Models of Melodic Analysis: Challenging the Gestalt Principles
Bod, R. (2002) · doi:10.1076/jnmr.31.1.27.8106 · Cited by 102
“Much research in unsupervised language learning is concerned with bootstrapping syntactic structure on the basis of pattern similarity and statistics from large language corpora (e.g., Finch & Chater, 1994; Brent & Cartwright, 1996; van Zaanen, 2000).”
BackgroundWord segmentation with universal prosodic cues.
Endress, A., & Hauser, M. (2010) · Cognitive Psychology · doi:10.1016/j.cogpsych.2010.05.001 · Cited by 99
Theories of early language acquisition.
Plunkett, K. (1997) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(97)01039-5 · Cited by 99
Dahan, D., & Brent, M. R. (1999) · Journal of experimental psychology. General · doi:10.1037//0096-3445.128.2.165 · Cited by 94
Self-citationThe generalized universal law of generalization
Chater, N., & Vitanyi, P. M. B. (2003) · Journal of Mathematical Psychology · doi:10.1016/s0022-2496(03)00013-0 · Cited by 92
“…practical generalization of the universal law [of generalization]: if we randomly pick items a and b , where we allow the most complex objects, then with overwhelming probability the universal law of generalization holds with the internal psychological space metric being the information metric.”
Background(Non)words, (non)words, (non)words: evidence for a protolexicon during the first year of life
Ngon, C., Martin, A. T., Emmanuel Dupoux, A., CABROL, D., Dutat, M., & Sharon Peperkamp, A. (2012) · Developmental Science · doi:10.1111/j.1467-7687.2012.01189.x · Cited by 99
“As to computational models of word segmentation, several techniques have been implemented, including minimum description length compression (Brent & Cartwright, 1996), mutual information over syllables (Swingley, 2005), and syllable chunking (Perruchet & Vinter, 1998; Batchelder, 2002).”
The perception of word boundaries in a second language
Altenberg, E. P. (2005) · doi:10.1191/0267658305sr250oa · Cited by 91
“It is unlikely that listeners rely on only one kind of cue when segmenting speech (e.g., Cutler et al., 1986; Grosjean and Gee, 1987; Brent and Cartwright, 1996; Vroomen et al., 1996; McQueen, 1998; Sanders and Neville, 2000).”
BackgroundHow humans learn and represent networks
Lynn, C. W., & Bassett, D. S. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912328117 · Cited by 87
“For example, consider spoken language, wherein distinct syllables transition from one to another in a continuous stream without pauses or demarcations between words (30).”
BackgroundWords in puddles of sound: modelling psycholinguistic effects in speech segmentation
Padraic Monaghan, B., & Christiansen, M. H. (2010) · Journal of Child Language · doi:10.1017/s0305000909990511 · Cited by 87
“…the performance of the model we used the complementary measures of PRECISION and RECALL, which have been used as conservative measures of model performance in previous research (e.g. Batchelder, 2002; Brent & Cartwright, 1996 ; Christiansen et al., 1998 ; Hockema, 2006 ; Venkataraman, 2001).”
InfluentialInference of variable-length linguistic and acoustic units by multigrams
Deligne, S., & Bimbot, F. (1997) · Speech Communication · doi:10.1016/s0167-6393(97)00048-4 · Cited by 88
“Cartwright and Brent Cartwright .and Brent, 1994; Brent and Cartwright, 1996 , for instance, propose to segment the text data by following a MDL approach.”
BackgroundInfants' sensitivity to word boundaries in fluent speech
Myers, J., Jusczyk, P., Nelson, D. G. K., Charles-Luce, J., Woodward, A., & Hirsh-Pasek, K. (1996) · Journal of Child Language · doi:10.1017/s0305000900010072 · Cited by 84
Unsupervised word discovery from speech using automatic segmentation into syllable-like units
Räsänen, O. J., Doyle, G., & Frank, M. C. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-645 · Cited by 74
Language proficiency and morpho-orthographic segmentation
Beyersmann, E., Casalis, S., Ziegler, J. C., & Jonathan Grainger, B. (2014) · Psychonomic Bulletin & Review · doi:10.3758/s13423-014-0752-9 · Cited by 91
Learning Diphone-Based Segmentation
Daland, R., & Pierrehumbert, J. (2011) · Cognitive Sciences · doi:10.1111/j.1551-6709.2010.01160.x · Cited by 81
“Cairns et al. (1997) did not regard the diphone model as a suitable model for acquisition, because calculating the model parameters depends on knowing the relative frequency with which word boundaries span different diphones.”
BackgroundInfluential7 Explaining Universal Tendencies and Language Particulars in Analogical Change
Albright, A. (2008) · doi:10.1093/acprof:oso/9780199298495.003.0007 · Cited by 124
“8Some models that take on the task of word segmentation include Allen and Christiansen (1996), Brent and Cartwright (1996) Cairns, Shillcock, Chater, and Levy (1997), and Brent (1999).”
Background
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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