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A tool for efficient and accurate segmentation of speech data: Announcing POnSS

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Rodd,  Joe
Psychology of Language Department, MPI for Psycholinguistics, Max Planck Society;
Centre for Language Studies, Radboud University;
International Max Planck Research School for Language Sciences, MPI for Psycholinguistics, Max Planck Society;

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Decuyper,  Caitlin
Psychology of Language Department, MPI for Psycholinguistics, Max Planck Society;

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Bosker,  Hans R.
Psychology of Language Department, MPI for Psycholinguistics, Max Planck Society;

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Citation

Rodd, J., Decuyper, C., Bosker, H. R., & Ten Bosch, L. (2021). A tool for efficient and accurate segmentation of speech data: Announcing POnSS. Behavior Research Methods, 53, 744-756. doi:10.3758/s13428-020-01449-6.


Cite as: https://hdl.handle.net/21.11116/0000-0006-A29C-3
Abstract
Despite advances in automatic speech recognition (ASR), human input is still essential to produce research-grade segmentations of speech data. Con- ventional approaches to manual segmentation are very labour-intensive. We introduce POnSS, a browser-based system that is specialized for the task of segmenting the onsets and offsets of words, that combines aspects of ASR with limited human input. In developing POnSS, we identified several sub- tasks of segmentation, and implemented each of these as separate interfaces for the annotators to interact with, to streamline their task as much as possible. We evaluated segmentations made with POnSS against a base- line of segmentations of the same data made conventionally in Praat. We observed that POnSS achieved comparable reliability to segmentation us- ing Praat, but required 23% less annotator time investment. Because of its greater efficiency without sacrificing reliability, POnSS represents a distinct methodological advance for the segmentation of speech data.