Browsing by Author "Warlaumont, AS"

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    Developing a Cross-Cultural Annotation System and MetaCorpus for Studying Infants’ Real World Language Experience
    (Collabra: Psychology, 2021-05-25) Soderstrom, M; Casillas, M; Bergelson, E; Rosemberg, C; Alam, F; Warlaumont, AS; Bunce, J
    Recent issues around reproducibility, best practices, and cultural bias impact naturalistic observational approaches as much as experimental approaches, but there has been less focus on this area. Here, we present a new approach that leverages cross-laboratory collaborative, interdisciplinary efforts to examine important psychological questions. We illustrate this approach with a particular project that examines similarities and differences in children’s early experiences with language. This project develops a comprehensive start-to-finish analysis pipeline by developing a flexible and systematic annotation system, and implementing this system across a sampling from a “metacorpus” of audiorecordings of diverse language communities. This resource is publicly available for use, sensitive to cultural differences, and flexible to address a variety of research questions. It is also uniquely suited for use in the development of tools for automated analysis.
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    Virtual machines and containers as a platform for experimentation
    (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2016-01-01) Metze, F; Riebling, E; Warlaumont, AS; Bergelson, E
    Copyright © 2016 ISCA. Research on computational speech processing has traditionally relied on the availability of a relatively large and complex infrastructure, which encompasses data (text and audio), tools (feature extraction, model training, scoring, possibly on-line and off-line, etc.), glue code, and computing. Traditionally, it has been very hard to move experiments from one site to another, and to replicate experiments. With the increasing availability of shared platforms such as commercial cloud computing platforms or publicly funded super-computing centers, there is a need and an opportunity to abstract the experimental environment from the hardware, and distribute complete setups as a virtual machine, a container, or some other shareable resource, that can be deployed and worked with anywhere. In this paper, we discuss our experience with this concept and present some tools that the community might find useful. We outline, as a case study, how such tools can be applied to a naturalistic language acquisition audio corpus.