Search

Infants' ability to segment words in fluent speech is affected by their language experience. In this study we investigated the conditions under which infants can segment words in a non-native language. Using the Head-turn Preference Procedure, we found that monolingual English-learning 8-month-olds can segment bisyllabic words in Spanish (trochees and iambs) but not French (iambs). Our results are incompatible with accounts that rely on distributional learning, language rhythm similarity, or target word prosodic shape alone. Instead, we show that monolingual English-learning infants are able to segment words in a non-native language as long as words have stress, as is the case in English. More specifically, we show that even in a rhythmically different non-native language, English-learning infants can find words by detecting stressed syllables and treating them as word onsets or offsets. [ABSTRACT FROM AUTHOR]

A critical part of infants' ability to acquire any language involves segmenting continuous speech input into discrete word forms. Certain properties of words could provide infants with reliable cues to word boundaries. Here we investigate the potential utility of vowel harmony (VH), a phonological property whereby vowels within a word systematically exhibit similarity ("harmony") for some aspect of the way they are pronounced. We present evidence that infants with no experience of VH in their native language nevertheless actively use these patterns to generate hypotheses about where words begin and end in the speech stream. In two sets of experiments, we exposed infants learning English, a language without VH, to a continuous speech stream in which the only systematic patterns available to be used as cues to word boundaries came from syllable sequences that showed VH or those that showed vowel disharmony (dissimilarity). After hearing less than one minute of the streams, infants showed evidence of sensitivity to VH cues. These results suggest that infants have an experience-independent sensitivity to VH, and are predisposed to segment speech according to harmony patterns. We also found that when the VH patterns were more subtle (Experiment 2), infants required more exposure to the speech stream before they segmented based on VH, consistent with previous work on infants' preferences relating to processing load. Our findings evidence a previously unknown mechanism by which infants could discover the words of their language, and they shed light on the perceptual mechanisms that might be responsible for the emergence of vowel harmony as an organizing principle for the sound structure of words in many languages. [ABSTRACT FROM AUTHOR]

While American English infants typically segment words from fluent speech by 7.5-months, studies of infants from other language backgrounds have difficulty replicating this finding. One possible explanation for this cross-linguistic difference is that the input infants from different language backgrounds receive is not as infant-directed as American English infant-directed speech (Floccia et al., 2016). Against this background, the current study investigates whether German 7.5- and 9-month-old infants segment words from fluent speech when the input is prosodically similar to American English IDS. While 9-month-olds showed successful segmentation of words from exaggerated IDS, 7.5-month-olds did not. These findings highlight (a) the beneficial impact of exaggerated IDS on infant speech segmentation, (b) cross-linguistic differences in word segmentation that are based not just on the kind of input available to children and suggest (c) developmental differences in the role of IDS as an attentional spotlight in speech segmentation. [ABSTRACT FROM AUTHOR]

Over the first year, infants begin to learn the words of their language. Previous work suggests that certain statistical regularities in speech could help infants segment the speech stream into words, thereby forming a proto-lexicon that could support learning of the eventual vocabulary. However, computational models of word segmentation have typically been tested using language input that is much less variable than actual speech is. We show that using actual, transcribed pronunciations rather than dictionary pronunciations of the same speech leads to worse segmentation performance across models. We also find that phonologically variable input poses serious problems for lexicon building, because even correctly segmented word forms exhibit a complex, many-to-many relationship with speakers' intended words. Many phonologically distinct word forms were actually the same intended word, and many identical transcriptions came from different intended words. The fact that previous models appear to have substantially overestimated the utility of simple statistical heuristics suggests a need to consider the formation of the lexicon in infancy differently. [ABSTRACT FROM AUTHOR]

The division of labor hypothesis proposed by Nespor, Peña, and Mehler (2003) postulates that consonants are more important than vowels in lexical processing (when learning and recognizing words). This consonant bias (C-bias) is supported by many adult and toddler studies. However, some cross-linguistic variation has been found in toddlerhood, and various hypotheses have been proposed to account for the origin of the consonant bias, which make distinct predictions regarding its developmental trajectory during the first year of life. The present study evaluated these hypotheses by investigating the consonant bias in young French-learning infants, a language in which a consistent consonant bias is reported from 11months of age onward. Accordingly, in a series of word form segmentation experiments building on the fact that both 6- and 8-month-old French-learning infants can segment monosyllabic words, we investigated the relative impact of consonant and vowel mispronunciations on the recognition of segmented word forms at these two ages. Infants were familiarized with passages containing monosyllabic target words and then tested in different conditions all including consonant and/or vowel mispronunciations of the target words. Overall, our findings reveal a consonant bias at 8months, but an opposite vowel bias at 6months. These findings first establish that the consonant bias emerges between 6 and 8months of age in French-learning infants. Second, we discuss the factors that might explain such a developmental trajectory, highlighting the possible roles of pre-lexical and phonological acquisition. [ABSTRACT FROM AUTHOR]

The word segmentation paradigm originally designed by Jusczyk and Aslin (1995) has been widely used to examine how infants from the age of 7.5 months can extract novel words from continuous speech. Here we report a series of 13 studies conducted independently in two British laboratories, showing that British English-learning infants aged 8-10.5 months fail to show evidence of word segmentation when tested in this paradigm. In only one study did we find evidence of word segmentation at 10.5 months, when we used an exaggerated infant-directed speech style. We discuss the impact of variations in infant-directed style within and across languages in the course of language acquisition. [ABSTRACT FROM AUTHOR]

Abstract: Due to extensive variability in the phonetic realizations of words, there may be few or no proximal spectro-temporal cues that identify a word’s onset or even its presence. Dilley and Pitt (2010) showed that the rate of context speech, distal from a to-be-recognized word, can have a sizeable effect on whether or not a word is perceived. This investigation considered whether there is a distinct role for distal rhythm in the disappearing word effect. Listeners heard sentences that had a grammatical interpretation with or without a critical function word (FW) and transcribed what they heard (e.g., are in Jill got quite mad when she heard there are birds can be removed and Jill got quite mad when she heard their birds is still grammatical). Consistent with a perceptual grouping hypothesis, participants were more likely to report critical FWs when distal rhythm (repeating ternary or binary pitch patterns) matched the rhythm in the FW-containing region than when it did not. Notably, effects of distal rhythm and distal rate were additive. Results demonstrate a novel effect of distal rhythm on the amount of lexical material listeners hear, highlighting the importance of distal timing information and providing new constraints for models of spoken word recognition. [Copyright &y& Elsevier]

While the languages of the world differ in many respects, they share certain commonalties, which can provide insight on our shared cognition. Here, we explore the learnability consequences of one of the striking commonalities between languages. Across languages, word frequencies follow a Zipfian distribution, showing a power law relation between a word's frequency and its rank. While their source in language has been studied extensively, less work has explored the learnability consequences of such distributions for language learners. We propose that the greater predictability of words in this distribution (relative to less skewed distributions) can facilitate word segmentation, a crucial aspect of early language acquisition. To explore this, we quantify word predictability using unigram entropy, assess it across languages using naturalistic corpora of child-directed speech and then ask whether similar unigram predictability facilitates word segmentation in the lab. We find similar unigram entropy in child-directed speech across 15 languages. We then use an auditory word segmentation task to show that the unigram predictability levels found in natural language are uniquely facilitative for word segmentation for both children and adults. These findings illustrate the facilitative impact of skewed input distributions on learning and raise questions about the possible role of cognitive pressures in the prevalence of Zipfian distributions in language. [ABSTRACT FROM AUTHOR]

Abstract: Word frequencies in natural language follow a highly skewed Zipfian distribution, but the consequences of this distribution for language acquisition are only beginning to be understood. Typically, learning experiments that are meant to simulate language acquisition use uniform word frequency distributions. We examine the effects of Zipfian distributions using two artificial language paradigms—a standard forced-choice task and a new orthographic segmentation task in which participants click on the boundaries between words in contexts. Our data show that learners can identify word forms robustly across widely varying frequency distributions. In addition, although performance in recognizing individual words is predicted best by their frequency, a Zipfian distribution facilitates word segmentation in context: The presence of high-frequency words creates more chances for learners to apply their knowledge in processing new sentences. We find that computational models that implement “chunking” are more effective than “transition finding” models at reproducing this pattern of performance. [Copyright &y& Elsevier]

Abstract: Infants have been described as ‘statistical learners’ capable of extracting structure (such as words) from patterned input (such as language). Here, we investigated whether prior knowledge influences how infants track transitional probabilities in word segmentation tasks. Are infants biased by prior experience when engaging in sequential statistical learning? In a laboratory simulation of learning across time, we exposed 9- and 10-month-old infants to a list of either disyllabic or trisyllabic nonsense words, followed by a pause-free speech stream composed of a different set of disyllabic or trisyllabic nonsense words. Listening times revealed successful segmentation of words from fluent speech only when words were uniformly disyllabic or trisyllabic throughout both phases of the experiment. Hearing trisyllabic words during the pre-exposure phase derailed infants’ abilities to segment speech into disyllabic words, and vice versa. We conclude that prior knowledge about word length equips infants with perceptual expectations that facilitate efficient processing of subsequent language input. [Copyright &y& Elsevier]

Abstract: Word segmentation from continuous speech is a difficult task that is faced by human infants when they start to learn their native language. Several studies indicate that infants might use several different cues to solve this problem, including intonation, linguistic stress, and transitional probabilities between subsequent speech sounds. In this work, a computational model for word segmentation and learning of primitive lexical items from continuous speech is presented. The model does not utilize any a priori linguistic or phonemic knowledge such as phones, phonemes or articulatory gestures, but computes transitional probabilities between atomic acoustic events in order to detect recurring patterns in speech. Experiments with the model show that word segmentation is possible without any knowledge of linguistically relevant structures, and that the learned ungrounded word models show a relatively high selectivity towards specific words or frequently co-occurring combinations of short words. [Copyright &y& Elsevier]

Abstract: The ability to discover groupings in continuous stimuli on the basis of distributional information is present across species and across perceptual modalities. We investigate the nature of the computations underlying this ability using statistical word segmentation experiments in which we vary the length of sentences, the amount of exposure, and the number of words in the languages being learned. Although the results are intuitive from the perspective of a language learner (longer sentences, less training, and a larger language all make learning more difficult), standard computational proposals fail to capture several of these results. We describe how probabilistic models of segmentation can be modified to take into account some notion of memory or resource limitations in order to provide a closer match to human performance. [ABSTRACT FROM AUTHOR]

Abstract: Since the experiments of Saffran et al. [Saffran, J., Aslin, R., & Newport, E. (1996). Statistical learning in 8-month-old infants. Science, 274, 1926–1928], there has been a great deal of interest in the question of how statistical regularities in the speech stream might be used by infants to begin to identify individual words. In this work, we use computational modeling to explore the effects of different assumptions the learner might make regarding the nature of words – in particular, how these assumptions affect the kinds of words that are segmented from a corpus of transcribed child-directed speech. We develop several models within a Bayesian ideal observer framework, and use them to examine the consequences of assuming either that words are independent units, or units that help to predict other units. We show through empirical and theoretical results that the assumption of independence causes the learner to undersegment the corpus, with many two- and three-word sequences (e.g. what’s that, do you, in the house) misidentified as individual words. In contrast, when the learner assumes that words are predictive, the resulting segmentation is far more accurate. These results indicate that taking context into account is important for a statistical word segmentation strategy to be successful, and raise the possibility that even young infants may be able to exploit more subtle statistical patterns than have usually been considered. [Copyright &y& Elsevier]

How can infants detect where words or morphemes start and end in the continuous stream of speech? Previous computational studies have investigated this question mainly for English, where morpheme and word boundaries are often isomorphic. Yet in many languages, words are often multimorphemic, such that word and morpheme boundaries do not align. Our study employed corpora of two languages that differ in the complexity of inflectional morphology, Chintang (Sino-Tibetan) and Japanese (in Experiment 1), as well as corpora of artificial languages ranging in morphological complexity, as measured by the ratio and distribution of morphemes per word (in Experiments 2 and 3). We used two baselines and three conceptually diverse word segmentation algorithms, two of which rely purely on sublexical information using distributional cues, and one that builds a lexicon. The algorithms' performance was evaluated on both word- and morpheme-level representations of the corpora. Segmentation results were better for the morphologically simpler languages than for the morphologically more complex languages, in line with the hypothesis that languages with greater inflectional complexity could be more difficult to segment into words. We further show that the effect of morphological complexity is relatively small, compared to that of algorithm and evaluation level. We therefore recommend that infant researchers look for signatures of the different segmentation algorithms and strategies, before looking for differences in infant segmentation landmarks across languages varying in complexity. [ABSTRACT FROM AUTHOR]

Abstract: Many studies have shown that listeners can segment words from running speech based on conditional probabilities of syllable transitions, suggesting that this statistical learning could be a foundational component of language learning. However, few studies have shown a direct link between statistical segmentation and word learning. We examined this possible link in adults by following a statistical segmentation exposure phase with an artificial lexicon learning phase. Participants were able to learn all novel object–label pairings, but pairings were learned faster when labels contained high probability (word-like) or non-occurring syllable transitions from the statistical segmentation phase than when they contained low probability (boundary-straddling) syllable transitions. This suggests that, for adults, labels inconsistent with expectations based on statistical learning are harder to learn than consistent or neutral labels. In contrast, a previous study found that infants learn consistent labels, but not inconsistent or neutral labels. [Copyright &y& Elsevier]

Infants come to learn several hundreds of word forms by two years of age, and it is possible this involves carving these forms out from continuous speech. It has been proposed that the task is facilitated by the presence of prosodic boundaries. We revisit this claim by running computational models of word segmentation, with and without prosodic information, on a corpus of infant-directed speech. We use five cognitively-based algorithms, which vary in whether they employ a sub-lexical or a lexical segmentation strategy and whether they are simple heuristics or embody an ideal learner. Results show that providing expert-annotated prosodic breaks does not uniformly help all segmentation models. The sub-lexical algorithms, which perform more poorly, benefit most, while the lexical ones show a very small gain. Moreover, when prosodic information is derived automatically from the acoustic cues infants are known to be sensitive to, errors in the detection of the boundaries lead to smaller positive effects, and even negative ones for some algorithms. This shows that even though infants could potentially use prosodic breaks, it does not necessarily follow that they should incorporate prosody into their segmentation strategies, when confronted with realistic signals. [ABSTRACT FROM AUTHOR]

Learners often need to identify and remember recurring units in continuous sequences, but the underlying mechanisms are debated. A particularly prominent candidate mechanism relies on distributional statistics such as Transitional Probabilities (TPs). However, it is unclear what the outputs of statistical segmentation mechanisms are, and if learners store these outputs as discrete chunks in memory. We critically review the evidence for the possibility that statistically coherent items are stored in memory and outline difficulties in interpreting past research. We use Slone and Johnson's (2018) experiments as a case study to show that it is difficult to delineate the different mechanisms learners might use to solve a learning problem. Slone and Johnson (2018) reported that 8-month-old infants learned coherent chunks of shapes in visual sequences. Here, we describe an alternate interpretation of their findings based on a multiple-cue integration perspective. First, when multiple cues to statistical structure were available, infants' looking behavior seemed to track with the strength of the strongest one - backward TPs, suggesting that infants process multiple cues simultaneously and select the strongest one. Second, like adults, infants are exquisitely sensitive to chunks, but may require multiple cues to extract them. In Slone and Johnson's (2018) experiments, these cues were provided by immediate chunk repetitions during familiarization. Accordingly, infants showed strongest evidence of chunking following familiarization sequences in which immediate repetitions were more frequent. These interpretations provide a strong argument for infants' processing of multiple cues and the potential importance of multiple cues for chunk recognition in infancy. [ABSTRACT FROM AUTHOR]