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Word Segmentation: The Role of Distributional Cues
Open accessAbstract
One of the infant's first tasks in language acquisition is to discover the words embedded in a mostly continuous speech stream. This learning problem might be solved by using distributional cues to word boundaries—for example, by computing the transitional probabilities between sounds in the language input and using the relative strengths of these probabilities to hypothesize word boundaries. The learner might be further aided by language-specific prosodic cues correlated with word boundaries. As a first step in testing these hypotheses, we briefly exposed adults to an artificial language in which the only cues available for word segmentation were the transitional probabilities between syllables. Subjects were able to learn the words of this language. Furthermore, the addition of certain prosodic cues served to enhance performance. These results suggest that distributional cues may play an important role in the initial word segmentation of language learners.
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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 Saffran, Newport, & Aslin, 1996
In the citing paper
“We (Saffran et al., 1996) demonstrated, in an explicit-learning task, that adult subjects were able to use transitional probabilities to learn the multisyllabic words of an artificial language presented as a synthesized speech stream containing no other cues to word boundaries.”
From Incidental Language Learning: Listening (and Learning) Out of the Corner of Your Ear (1997) · cites this paper as Saffran et al., 1996
In the citing paper
“the input was quite impoverished, containing neither utterance boundaries nor the acoustic and prosodic cues that are probabilistically correlated with word boundaries in some languages”
From Incidental Language Learning: Listening (and Learning) Out of the Corner of Your Ear (1997) · cites this paper as Saffran et al., 1996
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InfluentialMorphological decomposition based on the analysis of orthography
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“When a set of pure tones was substituted for the speech syllables used in the original Saffran, Newport, and Aslin (1996) study, adults showed the same ability to learn tone sequences that they had shown for syllable sequences.”
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“…nature (either transitional probabilities, which were higher within words than across word boundaries, or frequency of cooccurrence, which were higher for the three syllable sequences within words than across words; cf. Saffran et al., 1996b; Saffran et al., 1997; Aslin, Saffran & Newport, 1998).”
InfluentialChildren's implicit learning of graphotactic and morphological regularities.
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“This option was provided to keep participants from becoming bored during the task, and has been used successfully (for slightly different purposes) by Saffran, Newport, and Aslin (1996).”
MethodOn-Line Individual Differences in Statistical Learning Predict Language Processing
Misyak, J., Christiansen, M. H., & Tomblin, J. (2010) · Front. Psychology · doi:10.3389/fpsyg.2010.00031 · Cited by 204
“…and psychological disorders (e.g., Reber, 1993; Cleeremans et al., 1998), with many studies showing remarkable similarities in infant, child, and adult learning (e.g., Cherry and Stadler, 1995; Saffran et al., 1996a,b; Thomas and Nelson, 2001; Fiser and Aslin, 2002a,b; Gómez, 2002; Saffran, 2002).”
BackgroundPadraic Monaghan, B., Christiansen, M. H., & Chater, N. (2007) · Cognitive Psychology · doi:10.1016/j.cogpsych.2006.12.001 · Cited by 221
Cracking the Language Code: Neural Mechanisms Underlying Speech Parsing
McNealy, K., Mazziotta, J. C., & Dapretto, M. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.5501-05.2006 · Cited by 203
“…specify word boundaries, research has shown that infants as young as 7.5 months of age can use statistical regularities such as the greater co-occurrence of syllables within words than between words to successfully parse a continuous stream of speech (Saffran et al., 1996a; Aslin et al., 1998).”
BackgroundMethodResultInfluentialAn interaction between prosody and statistics in the segmentation of fluent speech.
Shukla, M., Nespor, M., & Mehler, J. (2007) · Cognitive Psychology · doi:10.1016/j.cogpsych.2006.04.002 · Cited by 196
“In previous experiments (e.g., Saffran et al., 1996; Peña et al., 2002), where up to 6 ‘words’ were concatenated at random, the TP from the last syllable of one ‘word’ to the first syllable of another was 0.24 (since immediate repetitions are not allowed, each ‘word’ can be followed by one of the…”
BackgroundMethodResultInfluentialLinguistic Constraints on Statistical Computations
Bonatti, L., Peña, M., Nespor, M., & Mehler, J. (2005) · Psychology Science · doi:10.1111/j.0956-7976.2005.01556.x · Cited by 193
Cross-language differences in cue use for speech segmentation.
Tyler, M., & Cutler, A. (2009) · Journal of the Acoustical Society of America · doi:10.1121/1.3129127 · Cited by 184
Modeling human performance in statistical word segmentation.
Frank, M. C., Goldwater, S., Mansinghka, V. K., Griffiths, T., & Tenenbaum, J. (2010) · Cognition · doi:10.1016/j.cognition.2010.07.005 · Cited by 183
The 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
“Previous language-learning studies have shown similar findings for adults exposed to an artificial language and children or even infants exposed to the same type of language ( Saffran, Newport, & Aslin, 1996 ).”
ResultSelf-citationArciuli, J., & Simpson, I. C. (2010) · Developmental Science · doi:10.1111/j.1467-7687.2009.00937.x · Cited by 187
Finding Words and Rules in a Speech Stream
Toro, J. M., Nespor, M., Mehler, J., & Bonatti, L. (2008) · Psychology Science · doi:10.1111/j.1467-9280.2008.02059.x · Cited by 180
“Indeed, humans can compute statistical relations (in particular, transitional probabilities, or TPs) that can be useful to identify lexical elements in continuous speech (e.g., Saffran, Newport, & Aslin, 1996 ).”
BackgroundImplicit Statistical Learning: A Tale of Two Literatures
Christiansen, M. H. (2019) · Topics in Cognitive Science · doi:10.1111/tops.12332 · Cited by 167
First-language phonotactics in second-language listening.
Weber, A., & Cutler, A. (2006) · Journal of the Acoustical Society of America · doi:10.1121/1.2141003 · Cited by 176
Speciale, G., Ellis, N. C., & Bywater, T. J. (2004) · Applied Psycholinguistics · doi:10.1017/s0142716404001146 · Cited by 151
Changing Structures in Midstream: Learning Along the Statistical Garden Path
Gebhart, A. L., Aslin, R. Ν., & Newport, E. L. (2009) · Cognitive Science · doi:10.1111/j.1551-6709.2009.01041.x · Cited by 191
Self-citationThe Roles of Word Stress and Vowel Harmony in Speech Segmentation
Vroomen, J., Tuomainen, J., & de Gelder, B. (1998) · doi:10.1006/jmla.1997.2548 · Cited by 166
“The 1 Saffran, Newport, and Aslin (1996) used lengthening effect of vowel harmony and all other interacof the vowel as a cue for lexical stress.”
BackgroundSpeech segmentation and word discovery: a computational perspective
Brent, M. R. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01350-9 · Cited by 163
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
“…(1996) computational Incremental Distributional Regularity Optimization (INCDROP) model suggests and data from Saffran, Aslin, et al. (1996) and Saffran, Newport, and Aslin (1996) illustrate, high transitional probability between speech segments tends to promote cohesion in the percept ,…”
BackgroundStatistical Learning of Adjacent and Non-Adjacent Dependencies among Non-Linguistic Sounds
Gebhart, A. L., Newport, E., & Aslin, R. (2009) · Psychonomic Bulletin & Review · doi:10.3758/pbr.16.3.486 · Cited by 157
Self-citationSpeech errors reflect newly learned phonotactic constraints.
Warker, J. A., & Dell, G. (2006) · Journal of Experimental Psychology. Learning, Memory and Cognition · doi:10.1037/0278-7393.32.2.387 · Cited by 157
Phonological Memory and Rule Learning
Williams, J. N., & Lovatt, P. (2003) · Language Learning · doi:10.1111/1467-9922.00211 · Cited by 156
Online neural monitoring of statistical learning
Batterink, L. J., & Paller, K. A. (2017) · Cortex; a journal devoted to the study of the nervous system and behavior · doi:10.1016/j.cortex.2017.02.004 · Cited by 155
“Learners can discover word boundaries by computing the transitional probabilities between neighboring syllables, which are higher within words compared to across word boundaries (Saffran et al., 1996a, 1996b).”
BackgroundMethod
Metadata
- Authors
- Saffran, J. R., Newport, E. L., & Aslin, R. N.
- Affiliations
- University of Rochester (US)
- Venue
- Journal of Memory and Language
- Publisher
- Elsevier BV
- Published
- 1996-08
- Volume
- 35
- Issue
- 4
- Pages
- 606-621
- ISSN
- 0749-596X (ISSN-L)
- OpenAlex
- W2115867364
- Cited by
- 1,405
- References
- 62
- Citations by year
- 2026: 35 · 2025: 49 · 2024: 109 · 2023: 43 · 2022: 52 · 2021: 52 · 2020: 67 · 2019: 104 · 2018: 41 · 2017: 95 · 2016: 44 · 2015: 62 · 2014: 65 · 2013: 56 · 2012: 51
- FWCI
- 19.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Memory and Language (publishedVersion, cc-by-nc-nd)
- 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
- word segmentation, distributional cues, transition probabilities, prosodic cues, artificial language learning, infant language acquisition, word boundary detection, statistical learning
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