Towards automated babble metrics
There are lots of good reasons to want to track the development of infant vocalizations — see e.g. Zwaigenbaum et al. "Clinical assessment and management of toddlers with suspected autism spectrum disorder" (2009). But existing methods are expensive and time-consuming — see e.g. Nyman and Lohmander, "Babbling in children with neurodevelopmental disability and validity of a simplified way of measuring canonical babbling ratio" (2018). (It's also unfortunately true that there's not yet any available dataset documenting the normal development of infant vocalizations from cooing and gooing to "canonical babbling", but that's another issue…)
People are starting to make and share extensive recordings of infant vocal development — see e.g. Frank et al., "A collaborative approach to infant research: Promoting reproducibility, best practices, and theory‐building" (2017). But automatic detection and classification of vocalization sources and types is still imperfect at best. And if we had reliable detection and classification methods, that would open up a new set of questions: Are the standard categories (e.g. "canonical babbling") really well defined and well separated? Do infant vocalizations of whatever type have measurable properties that would help to characterize and quantify normal or abnormal development?
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