Browsing by Subject "RELIABILITY"

Now showing 1 - 4 of 4
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    A Collaborative Approach to Infant Research: Promoting Reproducibility, Best Practices, and Theory-Building.
    (Infancy : the official journal of the International Society on Infant Studies, 2017-07) Frank, Michael C; Bergelson, Elika; Bergmann, Christina; Cristia, Alejandrina; Floccia, Caroline; Gervain, Judit; Hamlin, J Kiley; Hannon, Erin E; Kline, Melissa; Levelt, Claartje; Lew-Williams, Casey; Nazzi, Thierry; Panneton, Robin; Rabagliati, Hugh; Soderstrom, Melanie; Sullivan, Jessica; Waxman, Sandra; Yurovsky, Daniel
    The ideal of scientific progress is that we accumulate measurements and integrate these into theory, but recent discussion of replicability issues has cast doubt on whether psychological research conforms to this model. Developmental research-especially with infant participants-also has discipline-specific replicability challenges, including small samples and limited measurement methods. Inspired by collaborative replication efforts in cognitive and social psychology, we describe a proposal for assessing and promoting replicability in infancy research: large-scale, multi-laboratory replication efforts aiming for a more precise understanding of key developmental phenomena. The ManyBabies project, our instantiation of this proposal, will not only help us estimate how robust and replicable these phenomena are, but also gain new theoretical insights into how they vary across ages, linguistic communities, and measurement methods. This project has the potential for a variety of positive outcomes, including less-biased estimates of theoretically important effects, estimates of variability that can be used for later study planning, and a series of best-practices blueprints for future infancy research.
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    Automatic word count estimation from daylong child-centered recordings in various language environments using language-independent syllabification of speech
    (Speech Communication, 2019-10-01) Räsänen, O; Seshadri, S; Karadayi, J; Riebling, E; Bunce, J; Cristia, A; Metze, F; Casillas, M; Rosemberg, C; Bergelson, E; Soderstrom, M
    © 2019 The Authors 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.
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    Persistent Depressive Symptoms are Independent Predictors of Low-Grade Inflammation Onset Among Healthy Individuals.
    (Arquivos brasileiros de cardiologia, 2017-06) Franco, Fábio Gazelato de Mello; Laurinavicius, Antonio Gabriele; Lotufo, Paulo A; Conceição, Raquel D; Morita, Fernando; Katz, Marcelo; Wajngarten, Maurício; Carvalho, José Antonio Maluf; Bosworth, Hayden B; Santos, Raul Dias

    Background

    Depressive symptoms are independently associated with an increased risk of cardiovascular disease (CVD) among individuals with non-diagnosed CVD. The mechanisms underlying this association, however, remain unclear. Inflammation has been indicated as a possible mechanistic link between depression and CVD.

    Objectives

    This study evaluated the association between persistent depressive symptoms and the onset of low-grade inflammation.

    Methods

    From a database of 1,508 young (mean age: 41 years) individuals with no CVD diagnosis who underwent at least two routine health evaluations, 134 had persistent depressive symptoms (Beck Depression Inventory - BDI ≥ 10, BDI+) and 1,374 had negative symptoms at both time points (BDI-). All participants had been submitted to repeated clinical and laboratory evaluations at a regular follow-up with an average of 26 months from baseline. Low-grade inflammation was defined as plasma high-sensitivity C-Reactive Protein (CRP) concentrations > 3 mg/L. The outcome was the incidence of low-grade inflammation evaluated by the time of the second clinical evaluation.

    Results

    The incidence of low-grade inflammation was more frequently observed in the BDI+ group compared to the BDI- group (20.9% vs. 11.4%; p = 0.001). After adjusting for sex, age, waist circumference, body mass index, levels of physical activity, smoking, and prevalence of metabolic syndrome, persistent depressive symptoms remained an independent predictor of low-grade inflammation onset (OR = 1.76; 95% CI: 1.03-3.02; p = 0.04).

    Conclusions

    Persistent depressive symptoms were independently associated with low-grade inflammation onset among healthy individuals.
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    The Prevalence of Incidental and Symptomatic Lumbar Synovial Facet Cysts
    (Clinical Spine Surgery: A Spine Publication, 2018-06) Janssen, Stein J; Ogink, Paul T; Schwab, Joseph H