The Role of Prosody and Speech Register in Word Segmentation: A
            Computational Modelling Perspective

Ludusan, Bogdan; Mazuka, Reiko; Bernard, Mathieu; Cristià, Alejandrina; Dupoux, Emmanuel

doi:10.18653/v1/p17-2028

Cited by 9 publications

(13 citation statements)

References 17 publications

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“…prosody larger prosodic breaks (Ludusan et al, 2016) more difficult word form clustering (Exp. 3) easier word segmentation (Ludusan et al, 2017) phonetic categories discriminative learning clustering protolexicon segmentation Figure 6. Summary of IDS characteristics relative to ADS in a top-down model of phonetic category learning for the RIKEN corpus.…”

Section: Caregiver Infantmentioning

confidence: 99%

Are Words Easier to Learn From Infant‐ Than Adult‐Directed Speech? A Quantitative Corpus‐Based Investigation

et al. 2018

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We investigate whether infant-directed speech (IDS) could facilitate word form learning when compared to adult-directed speech (ADS). To study this, we examine the distribution of word forms at two levels, acoustic and phonological, using a large database of spontaneous speech in Japanese. At the acoustic level we show that, as has been documented before for phonemes, the realizations of words are more variable and less discriminable in IDS than in ADS. At the phonological level, we find an effect in the opposite direction: The IDS lexicon contains more distinctive words (such as onomatopoeias) than the ADS counterpart. Combining the acoustic and phonological metrics together in a global discriminability score reveals that the bigger separation of lexical categories in the phonological space does not compensate for the opposite effect observed at the acoustic level. As a result, IDS word forms are still globally less discriminable than ADS word forms, even though the effect is numerically small. We discuss the implication of these findings for the view that the functional role of IDS is to improve language learnability.

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Section: Caregiver Infantmentioning

confidence: 99%

Are Words Easier to Learn From Infant‐ Than Adult‐Directed Speech? A Quantitative Corpus‐Based Investigation

et al. 2018

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“…These studies are informative for a host of learnability questions, such as to test the sheer feasibility of a proposed word segmentation solution (Gambell & Yang, 2005), to compare alternative algorithms (Goldwater, Griffiths, & Johnson, 2009;Pearl, Goldwater, & Steyvers, 2010), to see whether languages differ in their intrinsic segmentability (Fourtassi, Börschinger, Johnson, & Dupoux, 2013), or whether child-directed speech is intrinsically easier to segment than adult-directed speech (Ludusan, Mazuka, Bernard, Cristia, & Dupoux, 2017). Additionally, there is emergent evidence suggesting that computational word segmentation results may also be relevant for infant psycholinguistics, by predicting the contents of infants' long-term vocabulary better than lexical status (Ngon et al, 2013) or pure frequency (Larsen, Cristia, & Dupoux, 2017).…”

Section: Introductionmentioning

confidence: 99%

WordSeg: Standardizing unsupervised word form segmentation from text

et al. 2019

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A basic task in first language acquisition likely involves discovering the boundaries between words or morphemes in input where these basic units are not overtly segmented. A number of unsupervised learning algorithms have been proposed in the last 20 years for these purposes, some of which have been implemented computationally, but whose results remain difficult to compare across papers. We created a tool that is open source, enables reproducible results, and encourages cumulative science in this domain. WordSeg has a modular architecture: It combines a set of corpora description routines, multiple algorithms varying in complexity and cognitive assumptions (including several that were not publicly available, or insufficiently documented), and a rich evaluation package. In the paper, we illustrate the use of this package by analyzing a corpus of child-directed speech in various ways, which further allows us to make recommendations for experimental design of follow-up work. Supplementary materials allow readers to reproduce every result in this paper, and detailed online instructions further enable them to go beyond what we have done. Moreover, the system can be installed within container software that ensures a stable and reliable environment. Finally, by virtue of its modular architecture and transparency, WordSeg can work as an open-source platform, to which other researchers can add their own segmentation algorithms.

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“…Batchelder (2002) applied a lexical algorithm onto the American English Bernstein Ratner corpus (Bernstein Ratner, 1984), and found a 15% advantage for CDS over ADS. Ludusan, Mazuka, Bernard, Cristia, and Dupoux (2017) applied two lexical and two sublexical algorithms to the Japanese-spoken Riken corpus (Mazuka, Igarashi, & Nishikawa, 2006), where the CDS advantage was between 2% and 10%. Still, it is unclear whether either corpus is representative of the CDS and ADS children hear every day.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we studied an ecological child-centered corpus containing both ADS and CDS. We followed Ludusan and colleagues (2017) by using both lexical and sublexical algorithms; in addition, we varied important parameters within these classes and further added two baselines. In all, we aimed to provide a more accurate and generalizable estimate of the size of segmentability differences in CDS versus ADS.…”

Section: Introductionmentioning

confidence: 99%

Segmentability Differences Between Child-Directed and Adult-Directed Speech: A Systematic Test With an Ecologically Valid Corpus

et al. 2019

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Previous computational modeling suggests it is much easier to segment words from child-directed speech (CDS) than adult-directed speech (ADS). However, this conclusion is based on data collected in the laboratory, with CDS from play sessions and ADS between a parent and an experimenter, which may not be representative of ecologically collected CDS and ADS. Fully naturalistic ADS and CDS collected with a nonintrusive recording device as the child went about her day were analyzed with a diverse set of algorithms. The difference between registers was small compared to differences between algorithms; it reduced when corpora were matched, and it even reversed under some conditions. These results highlight the interest of studying learnability using naturalistic corpora and diverse algorithmic definitions.

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The Role of Prosody and Speech Register in Word Segmentation: A Computational Modelling Perspective

Cited by 9 publications

References 17 publications

Are Words Easier to Learn From Infant‐ Than Adult‐Directed Speech? A Quantitative Corpus‐Based Investigation

Are Words Easier to Learn From Infant‐ Than Adult‐Directed Speech? A Quantitative Corpus‐Based Investigation

WordSeg: Standardizing unsupervised word form segmentation from text

Segmentability Differences Between Child-Directed and Adult-Directed Speech: A Systematic Test With an Ecologically Valid Corpus

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