Abstract
The chief objective of the study was to observe phrasing behaviour of transformer-based neural networks from the linguistic point of view. The transformer-based architecture mapped prosodic phrasing in isolated sentences read out on request, but was commanded to predict prosodic phrases in continuous texts of journalistic style taken from radio news bulletins. The transfer was quite successful in that most of the prosodic phrase boundaries in the actual newsreading (established by expert auditory analysis) were correctly suggested by the machine. This result is not unexpected as both genres belong to clearly enunciated informative speaking style. The outcome partially rehabilitates the so-called laboratory speech, which is sometimes branded as ecologically invalid. The follow-up analyses revealed that the differences between human phrasing in news bulletins and the partition suggested by the machine can be classified into meaningful linguistic categories based on the syntactic structure or semantic contents, and as such, they can inform further research design.
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Acknowledgments
This work was funded by Czech Science Foundation (GA–CR), project GA21-14758S, and by the grant of the University of West Bohemia, project No. SGS-2019-027. Computational resources were supplied by project “e-Infrastruktura CZ” (e-INFRA LM2018140) within Projects of Large Research, Development and Innovations Infrastructures.
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Volín, J., Řezáčková, M., Matouřek, J. (2021). Human and Transformer-Based Prosodic Phrasing in Two Speech Genres. In: Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2021. Lecture Notes in Computer Science(), vol 12997. Springer, Cham. https://doi.org/10.1007/978-3-030-87802-3_68
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