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This study aims to extend statistical learning (SL) research to multilinguals and provide an insight into what could facilitate word segmentation. We studied how the number of cues available in the input as well as the number of languages spoken influence SL and word segmentation. We used two SL tasks: one involving the tracking of transitional probabilities (TPs) between syllables of words, and another involving the tracking of two congruent cues-syllables and tones – in an artificial tone language. Data was collected from monolinguals, bilinguals, trilinguals, and quadrilinguals. Our results indicate that all language groups demonstrated similar SL capacity when segmenting words using TPs of syllables. However, when an additional cue was added, bilinguals, trilinguals, and quadrilinguals outperformed monolinguals. Interestingly, quadrilinguals also outperformed bilinguals. Performance was best for all groups when the input afforded two cues. This study suggests that while experience with multiple languages does not affect core SL ability, it enhances the tracking of multiple cues. The study further indicates that SL is affected by the number of cues available in the input as we found that performance was facilitated by the presence of two congruent cues. [ABSTRACT FROM AUTHOR]

One of the first tasks in language acquisition is word segmentation, a process to extract word forms from continuous speech streams. Statistical approaches to word segmentation have been shown to be a powerful mechanism, in which word boundaries are inferred from sequence statistics. This approach requires the learner to represent the frequency of units from syllable sequences, though accounts differ on how much statistical exposure is required. In this study, we examined the computational limit with which words can be extracted from continuous sequences. First, we discussed why two occurrences of a word in a continuous sequence is the computational lower limit for this word to be statistically defined. Next, we created short syllable sequences that contained certain words either two or four times. Learners were presented with these syllable sequences one at a time, immediately followed by a test of the novel words from these sequences. We found that, with the computationally minimal amount of two exposures, words were successfully segmented from continuous sequences. Moreover, longer syllable sequences providing four exposures to words generated more robust learning results. The implications of these results are discussed in terms of how learners segment and store the word candidates from continuous sequences. [ABSTRACT FROM AUTHOR]

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Infants' sensitivity to transitional probabilities (TPs) supports language development by facilitating mapping high-TP (HTP) words to meaning, at least up to 18 months of age. Here we tested whether this HTP advantage holds as lexical development progresses, and infants become better at forming word–referent mappings. Two groups of 24-month-olds (N = 64 and all White, tested in the United States) first listened to Italian sentences containing HTP and low-TP (LTP) words. We then used HTP and LTP words, and sequences that violated these statistics, in a mapping task. Infants learned HTP and LTP words equally well. They also learned LTP violations as well as LTP words, but learned HTP words better than HTP violations. Thus, by 2 years of age sensitivity to TPs does not lead to an HTP advantage but rather to poor mapping of violations of HTP word forms. Public Significance Statement: Learning words is a fundamental aspect of early language development. This experiment sheds light on how the mechanisms that support word learning change across time, and suggests that experience with patterns in speech relevant to finding word forms play an important role in mapping word forms to meaning. [ABSTRACT FROM AUTHOR]

The environment in which infants learn language is multimodal and rich with social cues. Yet, the effects of such cues, such as eye contact, on early speech perception have not been closely examined. This study assessed the role of ostensive speech, signalled through the speaker's eye gaze direction, on infants' word segmentation abilities. A familiarisation‐then‐test paradigm was used while electroencephalography (EEG) was recorded. Ten‐month‐old Dutch‐learning infants were familiarised with audio‐visual stories in which a speaker recited four sentences with one repeated target word. The speaker addressed them either with direct or with averted gaze while speaking. In the test phase following each story, infants heard familiar and novel words presented via audio‐only. Infants' familiarity with the words was assessed using event‐related potentials (ERPs). As predicted, infants showed a negative‐going ERP familiarity effect to the isolated familiarised words relative to the novel words over the left‐frontal region of interest during the test phase. While the word familiarity effect did not differ as a function of the speaker's gaze over the left‐frontal region of interest, there was also a (not predicted) positive‐going early ERP familiarity effect over right fronto‐central and central electrodes in the direct gaze condition only. This study provides electrophysiological evidence that infants can segment words from audio‐visual speech, regardless of the ostensiveness of the speaker's communication. However, the speaker's gaze direction seems to influence the processing of familiar words. Research Highlights: We examined 10‐month‐old infants' ERP word familiarity response using audio‐visual stories, in which a speaker addressed infants with direct or averted gaze while speaking.Ten‐month‐old infants can segment and recognise familiar words from audio‐visual speech, indicated by their negative‐going ERP response to familiar, relative to novel, words.This negative‐going ERP word familiarity effect was present for isolated words over left‐frontal electrodes regardless of whether the speaker offered eye contact while speaking.An additional positivity in response to familiar words was observed for direct gaze only, over right fronto‐central and central electrodes. [ABSTRACT FROM AUTHOR]

Infants learn language by exposure to streams of speech pro-duced by their caregivers. Early on, they manage to segmentword forms out of this continuous input, which is either di-rectly addressed to them, or directed to other adults, thus over-heard. It has been suggested that infant-directed speech is sim-plified and could facilitate language learning. This study aimedto investigate whether features such as utterance length, seg-mentation entropy and lexical diversity could account for anadvantage in segmentability of infant-directed speech. A largeset of word segmentation algorithms was used on an ecolog-ically valid corpus, consisting of 18 sets of recordings gath-ered from French-learning infants aged 3-48 months. A se-ries of textual analyses confirmed several simplicity featuresof infant-, compared to adult-directed speech. A small seg-mentation advantage was also documented, which could notbe attributed to any of those corpus features. Some particular-ities of the data invite further research on more corpora.

To explore whether current notions of statistically-basedlanguage learning could successfully scale to infants’linguistic experiences “in the wild”, we implemented astatistical-clustering word-segmentation model (Saffran et al.,1997) and sent its outputs to an implementation of a “frame”based form class tagger (Mintz, 2003) and, separately, to asimple word-order heuristic parser (Gervain et al., 2008). Wetested this pipeline model on various input types, rangingfrom quite idealized (orthographic words) to more naturalisticresyllabified corpora. We ask how these modeled capacitieswork together when they receive the noisy outputs ofupstream word finding processes as input, which more closelyresembles the scenario infants face in language acquisition.

There is growing evidence that COVID-19 brought changes that immediately affected early language development. Little is known for how long these COVID-19 related changes shaped development. The current study systematically and longitudinally addressed this issue, following up children's language development throughout the first 2.5 years. The present study follows up on the sample from Frota et al. (2022), which demonstrated that 7–9-month-old infants born and raised during the pandemic do not segment words unlike pre-pandemic peers. Four studies were conducted: (1) word segmentation task at 12 months of age (N pandemic = 15); (2) word learning task at 20 months of age (N pandemic = 20); (3) language and communication development up to 30 months of age, via CDI and CSBS parental reports (N pandemic ranged 25–74); (4) overall development at 30 months of age using the Griffiths Developmental Scales (N pandemic = 16). The pandemic sample consistently underperformed in all four studies in comparison to pre-pandemic data. There was no evidence of developed word segmentation abilities at 12 months of age, and no successful word learning at 20 months of age. Lexical development between 12 and 24 months of age was lower than in the pre-pandemic sample, while social communication did not seem to be affected. At 30 months of age, the pandemic sample showed lower scores and lower mental age on the Language and Communication Griffiths' subscale, in comparison to the pre-pandemic data. Infants born and raised during the pandemic have a poorer language development, that persists at least until 30 months of age. • We examined language development in children born and raised during COVID-19. • Findings were compared to data from pre-pandemic time. • Children born during COVID showed no word segmentation abilities at 12 months. • They failed in a word learning task at 20 months of age. • They also showed poorer vocabulary and communication skills until 30 months of age. [ABSTRACT FROM AUTHOR]

The acoustic variation in language presents learners with a substantial challenge. To learn by tracking statistical regularities in speech, infants must recognize words across tokens that differ based on characteristics such as the speaker's voice, affect, or the sentence context. Previous statistical learning studies have not investigated how these types of non-phonemic surface form variation affect learning. The present experiments used tasks tailored to two distinct developmental levels to investigate the robustness of statistical learning to variation. Experiment 1 examined statistical word segmentation in 11-month-olds and found that infants can recognize statistically segmented words across a change in the speaker's voice from segmentation to testing. The direction of infants' preferences suggests that recognizing words across a voice change is more difficult than recognizing them in a consistent voice. Experiment 2 tested whether 17-month-olds can generalize the output of statistical learning across variation to support word learning. The infants were successful in their generalization; they associated referents with statistically defined words despite a change in voice from segmentation to label learning. Infants' learning patterns also indicate that they formed representations of across word syllable sequences during segmentation. Thus, low probability sequences can act as object labels in some conditions. The findings of these experiments suggest that the units that emerge during statistical learning are not perceptually constrained, but rather are robust to naturalistic acoustic variation.

The acoustic variation in language presents learners with a substantial challenge. To learn by tracking statistical regularities in speech, infants must recognize words across tokens that differ based on characteristics such as the speaker's voice, affect, or the sentence context. Previous statistical learning studies have not investigated how these types of non-phonemic surface form variation affect learning. The present experiments used tasks tailored to two distinct developmental levels to investigate the robustness of statistical learning to variation. Experiment 1 examined statistical word segmentation in 11-month-olds and found that infants can recognize statistically segmented words across a change in the speaker's voice from segmentation to testing. The direction of infants' preferences suggests that recognizing words across a voice change is more difficult than recognizing them in a consistent voice. Experiment 2 tested whether 17-month-olds can generalize the output of statistical learning across variation to support word learning. The infants were successful in their generalization; they associated referents with statistically defined words despite a change in voice from segmentation to label learning. Infants' learning patterns also indicate that they formed representations of across word syllable sequences during segmentation. Thus, low probability sequences can act as object labels in some conditions. The findings of these experiments suggest that the units that emerge during statistical learning are not perceptually constrained, but rather are robust to naturalistic acoustic variation.

Research has shown that contoids (phonetically defined consonants) may provide more robust and reliable cues to syllable and word boundaries than vocoids (phonetically defined vowels). Recent studies of Danish, a language characterized by frequent long sequences of vocoids in speech, have suggested that the reduced occurrence of contoids may make speech in it intrinsically harder to segment than in closely related languages such as Norwegian. We addressed this hypothesis empirically in an artificial language learning experiment with native speakers of Danish, Norwegian, and English. We tested whether artificial speech consisting of concatenated contoid–vocoid syllables is easier to segment than speech consisting of vocoid–vocoid syllables where the first segment is a semivowel and the second a full vowel. Contrary to what was expected, we found no effect of the phonetic makeup of the syllables on speech segmentability. Possible interpretations and implications of this result are discussed. Open Practices: This article has been awarded Open Materials and Open Data badges. All materials and data are publicly accessible via the Open Science Framework at https://osf.io/rmjtg. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki. [ABSTRACT FROM AUTHOR]

We report the first set of results in a multi-year project to assess the robustness – and the factors promoting robustness – of the adult statistical word segmentation literature. This includes eight total experiments replicating six different experiments. The purpose of these replications is to assess the reproducibility of reported experiments, examine the replicability of their results, and provide more accurate effect size estimates. Reproducibility was mixed, with several papers either lacking crucial details or containing errors in the description of method, making it difficult to ascertain what was done. Replicability was also mixed: although in every instance we confirmed above-chance statistical word segmentation, many theoretically important moderations of that learning failed to replicate. Moreover, learning success was generally much lower than in the original studies. In the General Discussion, we consider whether these differences are due to differences in subject populations, low power in the original studies, or some combination of these and other factors. We also consider whether these findings are likely to generalize to the broader statistical word segmentation literature.

Abstract: Recent work asked if visual cues facilitate word segmentation in adults and infants (Thiessen, 2010). While adults showed better word segmentation when presented with a regular visual cue (consistent mapping between words and objects), infants did not. This difference was attributed to infants' lack of understanding that objects have labels. Alternatively, infants’ performance could reflect their difficulty with tracking and integrating multiple multimodal cues. We contrasted these two accounts by looking at the effect of visual cues on word segmentation in adults and across childhood (6–12 years). We found that older children (Mage 10;7) benefitted from the regular visual cues, but younger children (Mage 7;10), who already knew that objects have labels, did not. Knowing that objects have labels was not enough to use visual cues as an aid for segmentation. These findings show that the ability to integrate multimodal cues develops during childhood, and it is not yet adult‐like in children. [ABSTRACT FROM AUTHOR]

Abstract: Statistical learning is often taken to lie at the heart of many cognitive tasks, including the acquisition of language. One particular task in which probabilistic models have achieved considerable success is the segmentation of speech into words. However, these models have mostly been tested against English data, and as a result little is known about how a statistical learning mechanism copes with input regularities that arise from the structural properties of different languages. This study focuses on statistical word segmentation in Arabic, a Semitic language in which words are built around consonantal roots. We hypothesize that segmentation in such languages is facilitated by tracking consonant distributions independently from intervening vowels. Previous studies have shown that human learners can track consonant probabilities across intervening vowels in artificial languages, but it is unknown to what extent this ability would be beneficial in the segmentation of natural language. We assessed the performance of a Bayesian segmentation model on English and Arabic, comparing consonant‐only representations with full representations. In addition, we examined to what extent structurally different proto‐lexicons reflect adult language. The results suggest that for a child learning a Semitic language, separating consonants from vowels is beneficial for segmentation. These findings indicate that probabilistic models require appropriate linguistic representations in order to effectively meet the challenges of language acquisition. [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]

Spectral degradation reduces access to the acoustics of spoken language and compromises how learners break into its structure. We hypothesised that spectral degradation disrupts word segmentation, but that listeners can exploit other cues to restore detection of words. Normal-hearing adults were familiarised to artificial speech that was unprocessed or spectrally degraded by noise-band vocoding into 16 or 8 spectral channels. The monotonic speech stream was pause-free (Experiment 1), interspersed with isolated words (Experiment 2), or slowed by 33% (Experiment 3). Participants were tested on segmentation of familiar vs. novel syllable sequences and on recognition of individual syllables. As expected, vocoding hindered both word segmentation and syllable recognition. The addition of isolated words, but not slowed speech, improved segmentation. We conclude that syllable recognition is necessary but not sufficient for successful word segmentation, and that isolated words can facilitate listeners’ access to the structure of acoustically degraded speech. [ABSTRACT FROM PUBLISHER]

This study investigates a strategy based on predictability of consecutive sub-lexical units in learning to segment a continuous speech stream into lexical units using computational modeling and simulations. Lexical segmentation is one of the early challenges during language acquisition, and it has been studied extensively through psycholinguistic experiments as well as computational methods. However, despite strong empirical evidence, the explicit use of predictability of basic sub-lexical units in models of segmentation is underexplored. This paper presents an incremental computational model of lexical segmentation for exploring the usefulness of predictability for lexical segmentation. We show that the predictability cue is a strong cue for segmentation. Contrary to earlier reports in the literature, the strategy yields state-of-the-art segmentation performance with an incremental computational model that uses only this particular cue in a cognitively plausible setting. The paper also reports an in-depth analysis of the model, investigating the conditions affecting the usefulness of the strategy. [ABSTRACT FROM AUTHOR]

In natural settings, infants learn spoken language with the aid of a caregiver who explicitly provides social signals. Although previous studies have demonstrated that young infants are sensitive to these signals that facilitate language development, the impact of real-life interactions on early word segmentation and word-object mapping remains elusive. We tested whether infants aged 5-6 months and 9-10 months could segment a word from continuous speech and acquire a word-object relation in an ecologically valid setting. In Experiment 1, infants were exposed to a live tutor, while in Experiment 2, another group of infants were exposed to a televised tutor. Results indicate that both younger and older infants were capable of segmenting a word and learning a word-object association only when the stimuli were derived from a live tutor in a natural manner, suggesting that real-life interaction enhances the learning of spoken words in preverbal infants. [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]

Given that nouns rarely appear in isolation in French, infants acquiring the language must often retrieve the underlying representation of vowel-initial lexical forms from liaison contexts that provide conflicting information about the initial phoneme. Given this ambiguity, how do learners represent these nouns in their lexicons, and how do these representations change as learners' knowledge of liaison and the lexicon become more adult-like? To explore this question, we analyze the types of errors children make, in both naturalistic and elicited speech, and how these are affected by input frequency. In doing so, we evaluate two major proposals for how children's early representations of liaison develop. The first model, couched in a constructionist framework, predicts relatively late mastery of liaison (age five or older) and heavy dependence on the contexts in which a particular noun appears in the input. The second model takes an approach to liaison development that integrates it more closely with general phonological development and predicts relatively early mastery (by age three). The results o f a corpus study reveal that by age three children are correctly producing liaison in the nominal domain and that their production errors are consistent with a phonological model of liaison acquisition. An elicitation task demonstrates that three-year-olds succeed at learning and correctly apply their knowledge of liaison to new nouns following brief exposure, though their productions continue to be influenced by nouns' input distributions. Taken together, our findings suggest that by age three children are well on their way to adult-like representations of liaison. A phonologically based model, incorporating the effect of distributional context on early errors, provides a better overall fit to the data we present.* [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]

Recent studies have shown that English-, French-, and German-learning infants begin to use determiners to segment adjacent nouns before their first birthday. The present research extended the investigation to a typologically different language, Japanese, focusing on infants’ use of a high-frequency particle ga, a subject-marker. In Japanese, a particle follows, rather than precedes, the noun, and is usually followed by a predicate verb; thus particles rarely occur at utterance edges. Furthermore, a particle is not a free morpheme (like a determiner) but a bound morpheme. Although particles are frequently omitted in colloquial speech, the frequency of their occurrence is comparable to that of high-frequency determiners in a previously studied language, French. The results demonstrated that Japanese-learning infants used particles for word segmentation not at 10 and 12 months but at 15 months, which is later than the age at which infants begin to use determiners in previously studied languages. The reason for this delay was discussed in light of the properties of Japanese particles. [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]

Language is one of the most fascinating abilities that humans possess. Infants demonstrate an amazing repertoire of linguistic abilities from very early on and reach an adult-like form incredibly fast. However, language is not acquired all at once but in an incremental fashion. In this article we propose that the attentional system may be one of the sources for this developmental trajectory in language acquisition. At birth, infants are endowed with an attentional system fully driven by salient stimuli in their environment, such as prosodic information (e.g., rhythm or pitch). Early stages of language acquisition could benefit from this readily available, stimulus-driven attention to simplify the complex speech input and allow word segmentation. At later stages of development, infants are progressively able to selectively attend to specific elements while disregarding others. This attentional ability could allow them to learn distant non-adjacent rules needed for morphosyntactic acquisition. Because non-adjacent dependencies occur at distant moments in time, learning these dependencies may require correctly orienting attention in the temporal domain. Here, we gather evidence uncovering the intimate relationship between the development of attention and language. We aim to provide a novel approach to human development, bridging together temporal attention and language acquisition. [ABSTRACT FROM AUTHOR]

The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's cognitive plausibility. We suggest that though every computational model necessarily idealizes the modeled task, an informative language acquisition model can aim to be cognitively plausible in multiple ways. We discuss these cognitive plausibility checkpoints generally and then apply them to a case study in word segmentation, investigating a promising Bayesian segmentation strategy. We incorporate cognitive plausibility by using an age-appropriate unit of perceptual representation, evaluating the model output in terms of its utility, and incorporating cognitive constraints into the inference process. Our more cognitively plausible model shows a beneficial effect of cognitive constraints on segmentation performance. One interpretation of this effect is as a synergy between the naive theories of language structure that infants may have and the cognitive constraints that limit the fidelity of their inference processes, where less accurate inference approximations are better when the underlying assumptions about how words are generated are less accurate. More generally, these results highlight the utility of incorporating cognitive plausibility more fully into computational models of language acquisition. [ABSTRACT FROM AUTHOR]

To learn from their environments, infants must detect structure behind pervasive variation. This presents substantial and largely untested learning challenges in early language acquisition. The current experiments address whether infants can use statistical learning mechanisms to segment words when the speech signal contains acoustic variation produced by changes in speakers' voices. In Experiment 1, 8- and 10-month-old infants listened to a continuous stream of novel words produced by 8 different female voices. The voices alternated frequently, potentially interrupting infants' detection of transitional probability patterns that mark word boundaries. Infants at both ages successfully segmented words in the speech stream. In Experiment 2, 8-month-olds demonstrated the ability to generalize their learning about the speech stream when presented with a new, acoustically distinct voice during testing. However, in Experiments 3 and 4, when the same speech stream was produced by only 2 female voices, infants failed to segment the words. The results of these experiments indicate that low acoustic variation may interfere with infants' efficiency in segmenting words from continuous speech, but that infants successfully use statistical cues to segment words in conditions of high acoustic variation. These findings contribute to our understanding of whether statistical learning mechanisms can scale up to meet the demands of natural learning environments. [ABSTRACT FROM AUTHOR]

Human infants learn meanings for spoken words in complex interactions with other people, but the exact learning mechanisms are unknown. Among researchers, a widely studied learning mechanism is called cross-situational learning (XSL). In XSL, word meanings are learned when learners accumulate statistical information between spoken words and co-occurring objects or events, allowing the learner to overcome referential uncertainty after having sufficient experience with individually ambiguous scenarios. Existing models in this area have mainly assumed that the learner is capable of segmenting words from speech before grounding them to their referential meaning, while segmentation itself has been treated relatively independently of the meaning acquisition. In this article, we argue that XSL is not just a mechanism for word-to-meaning mapping, but that it provides strong cues for proto-lexical word segmentation. If a learner directly solves the correspondence problem between continuous speech input and the contextual referents being talked about, segmentation of the input into word-like units emerges as a by-product of the learning. We present a theoretical model for joint acquisition of proto-lexical segments and their meanings without assuming a priori knowledge of the language. We also investigate the behavior of the model using a computational implementation, making use of transition probability-based statistical learning. Results from simulations show that the model is not only capable of replicating behavioral data on word learning in artificial languages, but also shows effective learning of word segments and thenmeanings from continuous speech. Moreover, when augmented with a simple familiarity preference during learning, the model shows a good fit to human behavioral data in XSL tasks. These results support the idea of simultaneous segmentation and meaning acquisition and show that comprehensive models of early word segmentation should take referential word meanings into account. [ABSTRACT FROM AUTHOR]

The distal prosodic patterning established at the beginning of an utterance has been shown to influence downstream word segmentation and lexical access. In this study, we investigated whether distal prosody also affects word learning in a novel (artificial) language. Listeners were exposed to syllable sequences in which the embedded words were either congruent or incongruent with the distal prosody of a carrier phrase. Local segmentation cues, including the transitional probabilities between syllables, were held constant. During a test phase, listeners rated the items as either words or nonwords. Consistent with the perceptual grouping of syllables being predicted by distal prosody, congruent items were more likely to be judged as words than were incongruent items. The results provide the first evidence that perceptual grouping affects word learning in an unknown language, demonstrating that distal prosodic effects may be independent of lexical or other language-specific knowledge. [ABSTRACT FROM AUTHOR]

Rhythmicity characterizes both interpersonal synchrony and spoken language. Emotions and language are forms of interpersonal communication, which interact with each other throughout development. We investigated whether and how emotional synchrony between mothers and their 9-month-old infants relates to infants' word segmentation as an early marker of language development. Twenty-six 9-month-old infants and their German-speaking mothers took part in the study. To measure emotional synchrony, we coded positive, neutral and negative emotional expressions of the mothers and their infants during a free play session. We then calculated the degree to which the mothers' and their infants' matching emotional expressions followed a predictable pattern. To measure word segmentation, we familiarized infants with auditory text passages and tested how long they looked at the screen while listening to familiar versus novel words. We found that higher levels of predictability (i.e. low entropy) during mother-infant interaction is associated with infants' word segmentation performance. These findings suggest that individual differences in word segmentation relate to the complexity and predictability of emotional expressions during mother-infant interactions. [ABSTRACT FROM AUTHOR]

The acoustic variation in language presents learners with a substantial challenge. To learn by tracking statistical regularities in speech, infants must recognize words across tokens that differ based on characteristics such as the speaker’s voice, affect, or the sentence context. Previous statistical learning studies have not investigated how these types of surface form variation affect learning. The present experiments used tasks tailored to two distinct developmental levels to investigate the robustness of statistical learning to variation. Experiment 1 examined statistical word segmentation in 11-month-olds and found that infants can recognize statistically segmented words across a change in the speaker’s voice from segmentation to testing. The direction of infants’ preferences suggests that recognizing words across a voice change is more difficult than recognizing them in a consistent voice. Experiment 2 tested whether 17-month-olds can generalize the output of statistical learning across variation to support word learning. The infants were successful in their generalization; they associated referents with statistically defined words despite a change in voice from segmentation to label learning. Infants’ learning patterns also indicate that they formed representations of across-word syllable sequences during segmentation. Thus, low probability sequences can act as object labels in some conditions. The findings of these experiments suggest that the units that emerge during statistical learning are not perceptually constrained, but rather are robust to naturalistic acoustic variation.

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]

Purpose: In this study, the authors explored whether French-learning infants use nonadjacent phonotactic regularities in their native language, which they learn between the ages of 7 and 10 months, to segment words from fluent speech. Method: Two groups of 20 French-learning infants were tested using the head-turn preference procedure at 10 and 13 months of age. In Experiment 1, infants were familiarized with 2 passages: 1 containing a target word with a frequent nonadjacent phonotactic structure and the other containing a target word with an infrequent nonadjacent phonotactic structure in French. During the test phase, infants were presented with 4 word lists: 2 containing the target words presented during familiarization and 2 other control words with the same phonotactic structure. In Experiment 2, the authors retested infants’ ability to segment words with the infrequent phonotactic structure. Results: Ten- and 13-month-olds were able to segment words with the frequent phonotactic structure, but it is only by 13 months, and only under the circumstances of Experiment 2, that infants could segment words with the infrequent phonotactic structure. Conclusion: These results provide new evidence showing that infant word segmentation is influenced by prior nonadjacent phonotactic knowledge. [ABSTRACT FROM AUTHOR]

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]

This paper reconsiders the diphone-based word segmentation model of and , previously thought to be unlearnable. A statistically principled learning model is developed using Bayes' theorem and reasonable assumptions about infants' implicit knowledge. The ability to recover phrase-medial word boundaries is tested using phonetic corpora derived from spontaneous interactions with children and adults. The (unsupervised and semi-supervised) learning models are shown to exhibit several crucial properties. First, only a small amount of language exposure is required to achieve the model's ceiling performance, equivalent to between 1 day and 1 month of caregiver input. Second, the models are robust to variation, both in the free parameter and the input representation. Finally, both the learning and baseline models exhibit undersegmentation, argued to have significant ramifications for speech processing as a whole. [ABSTRACT FROM AUTHOR]

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: When listening to speech from one’s native language, words seem to be well separated from one another, like beads on a string. When listening to a foreign language, in contrast, words seem almost impossible to extract, as if there was only one bead on the same string. This contrast reveals that there are language-specific cues to segmentation. The puzzle, however, is that infants must be endowed with a language-independent mechanism for segmentation, as they ultimately solve the segmentation problem for any native language. Here, we approach the acquisition problem by asking whether there are language-independent cues to segmentation that might be available to even adult learners who have already acquired a native language. We show that adult learners recognize words in connected speech when only prosodic cues to word-boundaries are given from languages unfamiliar to the participants. In both artificial and natural speech, adult English speakers, with no prior exposure to the test languages, readily recognized words in natural languages with critically different prosodic patterns, including French, Turkish and Hungarian. We suggest that, even though languages differ in their sound structures, they carry universal prosodic characteristics. Further, these language-invariant prosodic cues provide a universally accessible mechanism for finding words in connected speech. These cues may enable infants to start acquiring words in any language even before they are fine-tuned to the sound structure of their native language. [ABSTRACT FROM AUTHOR]

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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]

We present a technique to automatically discover the word-sized) phone patterns that are present in speech utterances. These patterns are learnt from a set of phone lattices generated from the utterances. Just like children acquiring language, our system does not have prior information on what the meaningful patterns are. By applying the non-negative matrix factorization algorithm to a fixed-length high-dimensional vector representation of the speech utterances, a decomposition in terms of additive units is obtained. We illustrate that these units correspond to words in ease of a small vocabulary task. Our result also raises questions about whether explicit segmentation and clustering are needed in an unsupervised learning context. [ABSTRACT FROM AUTHOR]

The family size of the constituents of compound words, or the number of compounds sharing the constituents, affects English-speaking children's compound segmentation. This finding is consistent with a usage-based theory of language acquisition, whereby children learn abstract underlying linguistic structure through their experience with particular words. The family-size effect is particularly strong for the modifier or the leftmost element. The present study tested whether the effect of family size also holds for left-headed compounds as in French (e.g., chef de police“chief of police”) and whether the effect is due to headedness or left-to-right processing. Twenty-eight French-speaking children between 3;5 and 5;3 were asked to explain the meaning of existing compounds with constituents of varying family size. The children were more likely to mention a constituent when it came from a large family than a small family, suggesting that children's segmentation of compounds might be facilitated by analogy with existing compounds. Furthermore, as in the previous English study, children mentioned modifiers more often than heads, showing their sensitivity to the semantic roles of the constituents, rather than left-to-right processing. [ABSTRACT FROM AUTHOR]