Consequences of phonological variation for algorithmic word segmentation

Beech, Caroline; Swingley, Daniel

doi:10.1016/j.cognition.2023.105401

Cited by 1 publication

(1 citation statement)

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“…As a result, words often share reduced pronunciations with other words' canonical and reduced forms: for example, the three words heel, he'll , and hill all share commonly attested pronunciations in American English. These three types are not unusual—such variation‐driven entanglement is the predominant case (2/3 of types, 85% of tokens; Beech & Swingley, 2023). Even if children could identify speech sounds in context the same way native‐speaker corpus transcribers do, children would need additional principles to sort out phonology and the lexicon.…”

Section: Introductionmentioning

confidence: 99%

Computational Modeling of the Segmentation of Sentence Stimuli From an Infant Word‐Finding Study

Swingley,

Algayres

2024

Cognitive Science

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Computational models of infant word‐finding typically operate over transcriptions of infant‐directed speech corpora. It is now possible to test models of word segmentation on speech materials, rather than transcriptions of speech. We propose that such modeling efforts be conducted over the speech of the experimental stimuli used in studies measuring infants' capacity for learning from spoken sentences. Correspondence with infant outcomes in such experiments is an appropriate benchmark for models of infants. We demonstrate such an analysis by applying the DP‐Parser model of Algayres and colleagues to auditory stimuli used in infant psycholinguistic experiments by Pelucchi and colleagues. The DP‐Parser model takes speech as input, and creates multiple overlapping embeddings from each utterance. Prospective words are identified as clusters of similar embedded segments. This allows segmentation of each utterance into possible words, using a dynamic programming method that maximizes the frequency of constituent segments. We show that DP‐Parse mimics American English learners' performance in extracting words from Italian sentences, favoring the segmentation of words with high syllabic transitional probability. This kind of computational analysis over actual stimuli from infant experiments may be helpful in tuning future models to match human performance.

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Section: Introductionmentioning

confidence: 99%