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Conference Poster Year : 2023

Speech Maturity Dataset


Over the first years of life, children’s spontaneous vocal productions become increasingly adult-like, both in their shape and phonetic properties, and lay the foundation for later phonetic and phonological development. Yet, research in this area has been limited to a narrow set of languages and communities, mainly Indo-European languages from Western(ised) speaker communities, and focused on a narrow age range (0 - 24mo). We present a new publicly-available dataset, the Speech Maturity Dataset (SMD), consisting of 258,914 clips manually labelled for speaker and vocalisation type extracted from the long-form recordings of 398 children (209 male, 186 female) from 2 months to 6 years of age from 14 communities (ranging from rich industrialised societies to farmer-forager speaker communities) in 25+ languages. Albeit already massive, our dataset represents the first version of an ongoing and collaborative effort between field linguists, psycholinguists, and citizen scientists. The data set is expected to be expanded on a regular basis, since the project is still live (https://www.zooniverse.org/projects/laac-lscp/maturity-of-baby-sounds). SMD is a superset of the already existing BabbleCor dataset (Cychosz et al., 2019) which originally consisted of ~15k vocalisations. We followed the same methodology to constitute our dataset, whereby all the clips received a label based on the majority vote of at least 3 citizen scientists (i.e., non-scientific volunteers who devote time to annotate and label scientific data). Contrary to BabbleCor, which used the smaller and closed iHEARu-PLAY platform, we turned to the world's largest open citizen science platform, Zooniverse, as it had a larger and more diverse pool of citizen scientists. Citizen scientists labelled vocalisations taken from naturalistic long-form recordings with their vocalisation type: laughing, crying, canonical (speech-like vocalisation containing an adjacent consonant and vowel), non-canonical (speech-like vocalisation without an adjacent consonant and vowel), or junk (silence or non-human sounds). For a subset of the clips (N=110,577), citizen scientists also labelled the speaker type: baby (younger than 3 years), child (3-12 years), female/male adolescent (12-18 years), or female/male adult. SMD, which includes a wealth of metadata (child’s age/sex, linguistic environment, normativity, etc.), lends itself to several use cases. It can be used to study child vocalisation development at an unprecedented scale in a wide variety of communities, by computing indices of vocal development such as canonical proportion (i.e. the proportion of speech-like vocalizations that contain an adjacent consonant and vowel – regardless of whether they are in babble or meaningful speech) or linguistic proportion (i.e. the proportion of vocalizations that are speech-like). This dataset can also be used to train vocalisation-type classifiers in an effort to make software dedicated to the study of child language acquisition free, open-source, and reproducible. We showcase a potential use of this data set by presenting a preliminary analysis of canonical proportion and linguistic proportion. We fitted two linear mixed effect models to predict canonical proportion and separately, linguistic proportion from the child’s age, sex and monolingualism as fixed effects, and child ID nested in corpus as a random effect to account for individual variation. While for both models we observe a statistically significant positive effect of age (which is natural, as we expect these proportions to increase with age), we do not observe any significant effect of monolingualism or sex, suggesting that children follow a similar development trajectory. Results like these promise to allow researchers to significantly expand their knowledge of early vocal development.


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Dates and versions

halshs-04294803 , version 1 (20-11-2023)


  • HAL Id : halshs-04294803 , version 1


William N Havard, Loann Peurey, Kasia Hitczenko, Alejandrina Cristia. Speech Maturity Dataset. Many Paths to Language (MPaL) 2023, Oct 2023, Nijmegen, Netherlands. . ⟨halshs-04294803⟩
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