Computational simulation of CV combination preferences in babbling

Nam, Hosung; Goldstein, Louis; Giulivi, Sara; Levitt, Andrea G.; Whalen, D. H.

doi:10.1016/j.wocn.2012.11.002

Cited by 27 publications

(21 citation statements)

References 41 publications

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“…However, our modeling results show that there is systematicity in the off-diagonals that is well-predicted by Articulatory Phonology (AP), which can calculate the synergy between any two phones. Our modeling of F/C did not support the assumptions of that model (Nam, Giulivi, Goldstein, Levitt, & Whalen, submitted). Because of this systematicity, we will expand the investigation to the off-diagonals here.…”

Section: Introductioncontrasting

confidence: 83%

Biomechanically Preferred Consonant-Vowel Combinations Fail to Appear in Adult Spoken Corpora

et al. 2012

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Certain consonant/vowel (CV) combinations are more frequent than would be expected from the individual C and V frequencies alone, both in babbling and, to a lesser extent, in adult language, based on dictionary counts: Labial consonants co-occur with central vowels more often than chance would dictate; coronals co-occur with front vowels, and velars with back vowels (Davis & MacNeilage, 1994). Plausible biomechanical explanations have been proposed, but it is also possible that infants are mirroring the frequency of the CVs that they hear. As noted, previous assessments of adult language were based on dictionaries; these “type” counts are incommensurate with the babbling measures, which are necessarily “token” counts. We analyzed the tokens in two spoken corpora for English, two for French and one for Mandarin. We found that the adult spoken CV preferences correlated with the type counts for Mandarin and French, not for English. Correlations between the adult spoken corpora and the babbling results had all three possible outcomes: significantly positive (French), uncorrelated (Mandarin), and significantly negative (English). There were no correlations of the dictionary data with the babbling results when we consider all nine combinations of consonants and vowels. The results indicate that spoken frequencies of CV combinations can differ from dictionary (type) counts and that the CV preferences apparent in babbling are biomechanically driven and can ignore the frequencies of CVs in the ambient spoken language.

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Section: Introductioncontrasting

confidence: 83%

Biomechanically Preferred Consonant-Vowel Combinations Fail to Appear in Adult Spoken Corpora

et al. 2012

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“…So far these weights have been gleaned from subjective evaluation of the results of speech production experiments and through back-fitting of the weight parameters based on speech synthesis using the task dynamic simulator TADA (Task Dynamic Toolkit) (Nam et al, 2004). Another measure of synergy has recently been explored in simulating results from babbling, namely, that the distance between the target positions of different segments can be converted directly into a synergy measure (Nam et al, 2013).…”

Section: Measuring Synergymentioning

confidence: 99%

The coarticulation/invariance scale: Mutual information as a measure of coarticulation resistance, motor synergy, and articulatory invariance

Iskarous

Mooshammer²,

Hoole³

et al. 2013

The Journal of the Acoustical Society of America

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Coarticulation and invariance are two topics at the center of theorizing about speech production and speech perception. In this paper, a quantitative scale is proposed that places coarticulation and invariance at the two ends of the scale. This scale is based on physical information flow in the articulatory signal, and uses Information Theory, especially the concept of mutual information, to quantify these central concepts of speech research. Mutual Information measures the amount of physical information shared across phonological units. In the proposed quantitative scale, coarticulation corresponds to greater and invariance to lesser information sharing. The measurement scale is tested by data from three languages: German, Catalan, and English. The relation between the proposed scale and several existing theories of coarticulation is discussed, and implications for existing theories of speech production and perception are presented.

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“…It seems likely, therefore, that we will soon be able to exploit such databases also for other purposes, such as measuring the formants in vowel-like portions of the infants' vocalizations to replicate and extend several landmark studies of cross-language differences in vowel development (de Boysson-Bardies et al, 1989; Rvachew et al, 2006), or basing a more elaborated annotation method on the automatic tagging of the vocalization type. For example, imagine a set of modeling studies that extends Serkhane et al (2007) versus Nam et al (2013) to phonetically transcribed canonical babble produced by infants acquiring languages other than American English. The data might be got from daylong recordings gathered for other purposes, such as Canault et al (2015), by picking out just the vocalizations that have been identified as canonical babble in the recordings made between 6 and 12 months, using these as stimuli in a web-based experiment where several adults imitate each vocalization, and then applying standard ASR to the adult imitations to make a consensus phonetic transcription.…”

Section: Databases Of Infant-directed Speech and Infant “Speech”mentioning

confidence: 99%

Methods for eliciting, annotating, and analyzing databases for child speech development

Beckman

Plummer

Munson

et al. 2017

Computer Speech & Language

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Methods from automatic speech recognition (ASR), such as segmentation and forced alignment, have facilitated the rapid annotation and analysis of very large adult speech databases and databases of caregiver-infant interaction, enabling advances in speech science that were unimaginable just a few decades ago. This paper centers on two main problems that must be addressed in order to have analogous resources for developing and exploiting databases of young children's speech. The first problem is to understand and appreciate the differences between adult and child speech that cause ASR models developed for adult speech to fail when applied to child speech. These differences include the fact that children's vocal tracts are smaller than those of adult males and also changing rapidly in size and shape over the course of development, leading to between-talker variability across age groups that dwarfs the between-talker differences between adult men and women. Moreover, children do not achieve fully adult-like speech motor control until they are young adults, and their vocabularies and phonological proficiency are developing as well, leading to considerably more within-talker variability as well as more between-talker variability. The second problem then is to determine what annotation schemas and analysis techniques can most usefully capture relevant aspects of this variability. Indeed, standard acoustic characterizations applied to child speech reveal that adult-centered annotation schemas fail to capture phenomena such as the emergence of covert contrasts in children's developing phonological systems, while also revealing children's nonuniform progression toward community speech norms as they acquire the phonological systems of their native languages. Both problems point to the need for more basic research into the growth and development of the articulatory system (as well as of the lexicon and phonological system) that is oriented explicitly toward the construction of age-appropriate computational models.

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Computational simulation of CV combination preferences in babbling

Cited by 27 publications

References 41 publications

Biomechanically Preferred Consonant-Vowel Combinations Fail to Appear in Adult Spoken Corpora

Biomechanically Preferred Consonant-Vowel Combinations Fail to Appear in Adult Spoken Corpora

The coarticulation/invariance scale: Mutual information as a measure of coarticulation resistance, motor synergy, and articulatory invariance

Methods for eliciting, annotating, and analyzing databases for child speech development

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