Learners rely on a combination of experience-independent and experience-dependent mechanisms to extract information from the environment. Language acquisition involves both types of mechanisms, but most theorists emphasize the relative importance of experience-independent mechanisms. The present study shows that a fundamental task of language acquisition, segmentation of words from fluent speech, can be accomplished by 8-month-old infants based solely on the statistical relationships between neighboring speech sounds. Moreover, this word segmentation was based on statistical learning from only 2 minutes of exposure, suggesting that infants have access to a powerful mechanism for the computation of statistical properties of the language input.

https://science.sciencemag.org/content/sci/274/5294/1926.full.pdf

Statistical Learning by 8-Month-Old Infants

Statistical learning of tone sequences by human infants and adults.

A recent report demonstrated that 8-month-olds can seg- ment a continuous stream of speech syllables, containing no acoustic or prosodic cues to word boundaries, into wordlike units after only 2 min of listening experience (Saffran, Aslin, & Newport, 1996). Thus, a powerful learning mechanism capable of extracting statistical informa- tion from fluent speech is available early in development. The present study extends these results by documenting the particular type of statis- tical computation—transitional (conditional) probability—used by infants to solve this word-segmentation task. An artificial language corpus, consisting of a continuous stream of trisyllabic nonsense words, was presented to 8-month-olds for 3 min. A postfamiliarization test compared the infants' responses to words versus part-words (tri- syllabic sequences spanning word boundaries). The corpus was con- structed so that test words and part-words were matched in frequency, but differed in their transitional probabilities. Infants showed reliable discrimination of words from part-words, thereby demonstrating rapid segmentation of continuous speech into words on the basis of transi- tional probabilities of syllable pairs.

/pdf/computation-of-conditional-probability-statistics-by-8-month-290v3tm2md.pdf

Computation of Conditional Probability Statistics by 8-Month-Old Infants

The Beginnings of Word Segmentation in English-Learning Infants.

The ideas of frequency and predictability have played a fundamental role in models of human language processing for well over a hundred years (Schuchardt, 1885; Jespersen, 1922; Zipf, 1929; Martinet, 1960; Oldfield & Wingfield, 1965; Fidelholz, 1975; Jescheniak & Levelt, 1994; Bybee, 1996). While most psycholinguistic models have thus long included word frequency as a component, recent models have proposed more generally that probabilistic information about words, phrases, and other linguistic structure is represented in the minds of language users and plays a role in language comprehension (Jurafsky, 1996; MacDonald, 1993; McRae, Spivey-Knowlton, & Tanenhaus, 1998; Narayanan & Jurafsky, 1998; Trueswell & Tanenhaus, 1994) production (Gregory, Raymond, Bell, Fosler-Lussier, & Jurafsky, 1999; Roland & Jurafsky, 2000) and learning (Brent & Cartwright, 1996; Landauer & Dumais, 1997; Saffran, Aslin, & Newport, 1996; Seidenberg & MacDonald, 1999). In recent papers (Bell, Jurafsky, Fosler-Lussier, Girand, & Gildea, 1999; Gregory et al., 1999; Jurafsky, Bell, Fosler-Lussier, Girand, & Raymond, 1998), we have been studying the role of predictability and frequency in lexical production. Our goal is to understand the many factors that affect production variability as reflected in reduction processes such as vowel reduction, durational shortening, or final segmental deletion of words in spontaneous speech. One proposal that has resulted from this work is the Probabilistic Reduction Hypothesis: word forms are reduced when they have a higher probability. The probability of a word is conditioned on many aspects of its context, including neighboring words, syntactic and lexical structure, semantic expectations, and discourse factors. This proposal thus generalizes over earlier models which refer only to word frequency (Zipf, 1929; Fidelholz, 1975; Rhodes, 1992, 1996) or predictability (Fowler & Housum, 1987). In this paper we focus on a particular domain of probabilistic linguistic knowledge in lexical production: the role of local probabilistic relations between words.

Probabilistic relations between words: Evidence from reduction in lexical production.

Distributional regularity and phonotactic constraints are useful for segmentation

2006 – The Henry Edwin Sever Professor of Engineering, Washington U. Joint in Biomedical Engineering and Genetics 2004 – Professor in Computers Science, Washington University, Joint in Biomedical Engineering and Genetics 1999 – 2004 Associate Professor in Computer Science, Washington University, Joint in Biomedical Engineering and Genetics 1997 – 1999 Associate Professor in Cognitive Science, Johns Hopkins University 1991 – 1997 Assistant Professor in Cognitive Science, Johns Hopkins University

Lexical categorization: fitting template grammars by incremental MDL optimization

Infants face the difficult problem of segmenting continuous speech into words without the benefit of a fully developed lexicon. Several sources of information in speech might help infants solve this problem, including prosody, semantic correlations and phonotactics. Research to date has focused on determining to which of these sources infants might be sensitive, but little work has been done to determine the potential usefulness of each source. The computer simulations reported here are a first attempt to measure the usefulness of distributional and phonotactic information in segmenting phoneme sequences. The algorithms hypothesize different segmentations of the input into words and select the best hypothesis according to the Minimum Description Length principle. Our results indicate that while there is some useful information in both phoneme distributions and phonotactic rules, the combination of both sources is most useful.

Segmenting speech without a lexicon: The roles of phonotactics and speech source

Distributional regularity and phonotactics are useful for early lexical acquisition

Segmenting Speech without a Lexicon: Evidence for a Bootstrapping Model of Lexical Acquisition

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