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  A Multimodal Corpus of Expert Gaze and Behavior during Phonetic Segmentation Tasks

Khan, A., Steiner, I., Sugano, Y., Bulling, A., & Macdonald, R. (2017). A Multimodal Corpus of Expert Gaze and Behavior during Phonetic Segmentation Tasks. Retrieved from http://arxiv.org/abs/1712.04798.

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arXiv:1712.04798.pdf (Preprint), 490KB
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 Urheber:
Khan, Arif1, Autor
Steiner, Ingmar1, Autor
Sugano, Yusuke1, Autor
Bulling, Andreas2, Autor           
Macdonald, Ross1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society, ou_1116547              

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Schlagwörter: Computer Science, Human-Computer Interaction, cs.HC,Computer Science, Computation and Language, cs.CL
 Zusammenfassung: Phonetic segmentation is the process of splitting speech into distinct phonetic units. Human experts routinely perform this task manually by analyzing auditory and visual cues using analysis software, which is an extremely time-consuming process. Methods exist for automatic segmentation, but these are not always accurate enough. In order to improve automatic segmentation, we need to model it as close to the manual segmentation as possible. This corpus is an effort to capture the human segmentation behavior by recording experts performing a segmentation task. We believe that this data will enable us to highlight the important aspects of manual segmentation, which can be used in automatic segmentation to improve its accuracy.

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Sprache(n): eng - English
 Datum: 2017-12-132017
 Publikationsstatus: Online veröffentlicht
 Seiten: 4 p.
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 Identifikatoren: arXiv: 1712.04798
URI: http://arxiv.org/abs/1712.04798
BibTex Citekey: Khan2017
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