Publications

Abstract

- * indicates joint first authorship - Abstract: A range of demographic variables influence how much speech young children hear. However, because studies have used vastly different sampling methods, quantitative comparison of interlocking demographic effects has been nearly impossible, across or within studies. We harnessed a unique collection of existing naturalistic, day-long recordings from 61 homes across four North American cities to examine language input as a function of age, gender, and maternal education. We analyzed adult speech heard by 3- to 20-month-olds who wore audio recorders for an entire day. We annotated speaker gender and speech register (child-directed or adult-directed) for 10,861 utterances from female and male adults in these recordings. Examining age, gender, and maternal education collectively in this ecologically-valid dataset, we find several key results. First, the speaker gender imbalance in the input is striking: children heard 2--3x more speech from females than males. Second, children in higher-maternal-education homes heard more child-directed speech than those in lower-maternal education homes. Finally, our analyses revealed a previously unreported effect: the proportion of child-directed speech in the input increases with age, due to a decrease in adult-directed speech with age. This large-scale analysis is an important step forward in collectively examining demographic variables that influence early development, made possible by pooled, comparable, day-long recordings of children's language environments. The audio recordings, annotations, and annotation software are readily available for re-use and re-analysis by other researchers.

Abstract

Recent years have seen rapid technological development of devices that can record communicative behavior as participants go about daily life. This paper is intended as an end-to-end methodological guidebook for potential users of these technologies, including researchers who want to study children’s or adults’ communicative behavior in everyday contexts. We explain how long-format speech environment (LFSE) recordings provide a unique view on language use and how they can be used to complement other measures at the individual and group level. We aim to help potential users of these technologies make informed decisions regarding research design, hardware, software, and archiving. We also provide information regarding ethics and implementation, issues that are difficult to navigate for those new to this technology, and on which little or no resources are available. This guidebook offers a concise summary of information for new users and points to sources of more detailed information for more advanced users. Links to discussion groups and community-augmented databases are also provided to help readers stay up-to-date on the latest developments.

Abstract

Odor naming is enhanced in communities where communication about odors is a central part of daily life (e.g., wine experts, flavorists, and some hunter‐gatherer groups). In this study, we investigated how expert knowledge and daily experience affect the ability to name odors in a group of experts that has not previously been investigated in this context—Iranian herbalists; also called attars—as well as cooks and laypeople. We assessed naming accuracy and consistency for 16 herb and spice odors, collected judgments of odor perception, and evaluated participants' odor meta‐awareness. Participants' responses were overall more consistent and accurate for more frequent and familiar odors. Moreover, attars were more accurate than both cooks and laypeople at naming odors, although cooks did not perform significantly better than laypeople. Attars' perceptual ratings of odors and their overall odor meta‐awareness suggest they are also more attuned to odors than the other two groups. To conclude, Iranian attars—but not cooks—are better odor namers than laypeople. They also have greater meta‐awareness and differential perceptual responses to odors. These findings further highlight the critical role that expertise and type of experience have on olfactory functions.

Abstract

Automatic word count estimation (WCE) from audio recordings can be used to quantify the amount of verbal communication in a recording environment. One key application of WCE is to measure language input heard by infants and toddlers in their natural environments, as captured by daylong recordings from microphones worn by the infants. Although WCE is nearly trivial for high-quality signals in high-resource languages, daylong recordings are substantially more challenging due to the unconstrained acoustic environments and the presence of near- and far-field speech. Moreover, many use cases of interest involve languages for which reliable ASR systems or even well-defined lexicons are not available. A good WCE system should also perform similarly for low- and high-resource languages in order to enable unbiased comparisons across different cultures and environments. Unfortunately, the current state-of-the-art solution, the LENA system, is based on proprietary software and has only been optimized for American English, limiting its applicability. In this paper, we build on existing work on WCE and present the steps we have taken towards a freely available system for WCE that can be adapted to different languages or dialects with a limited amount of orthographically transcribed speech data. Our system is based on language-independent syllabification of speech, followed by a language-dependent mapping from syllable counts (and a number of other acoustic features) to the corresponding word count estimates. We evaluate our system on samples from daylong infant recordings from six different corpora consisting of several languages and socioeconomic environments, all manually annotated with the same protocol to allow direct comparison. We compare a number of alternative techniques for the two key components in our system: speech activity detection and automatic syllabification of speech. As a result, we show that our system can reach relatively consistent WCE accuracy across multiple corpora and languages (with some limitations). In addition, the system outperforms LENA on three of the four corpora consisting of different varieties of English. We also demonstrate how an automatic neural network-based syllabifier, when trained on multiple languages, generalizes well to novel languages beyond the training data, outperforming two previously proposed unsupervised syllabifiers as a feature extractor for WCE.