Abstract
This paper presents a two-step method of automatic prosodic boundary detection using both textual and acoustic features. Firstly, we predict possible boundary positions using textual features; secondly, we detect the actual boundaries at the predicted positions using acoustic features. For evaluation of the algorithms we use a 26-h subcorpus of CORPRES, a prosodically annotated corpus of Russian read speech. We have also conducted two independent experiments using acoustic features and textual features separately. Acoustic features alone enable to achieve the F\(_1\) measure of 0.85, precision of 0.94, recall of 0.78. Textual features alone work with the F\(_1\) measure of 0.84, precision of 0.84, recall of 0.83. The proposed two-step approach combining the two groups of features yields the efficiency of 0.90, recall of 0.85 and precision of 0.99. It preserves the high recall provided by textual information and the high precision achieved using acoustic information. This is the best published result for Russian.
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Notes
- 1.
Texts A, B and C comprise 75 % of all the recordings.
- 2.
We use the term “prosodic word” in its traditional sense for a content word and its clitics, which lose their lexical stress and form one rhythmic unit with the adjacent stressed word.
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The research is supported by the Russian Science Foundation (research grant # 14-18-01352).
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Kocharov, D., Kachkovskaia, T., Mirzagitova, A., Skrelin, P. (2016). Combining Syntactic and Acoustic Features for Prosodic Boundary Detection in Russian. In: Král, P., Martín-Vide, C. (eds) Statistical Language and Speech Processing. SLSP 2016. Lecture Notes in Computer Science(), vol 9918. Springer, Cham. https://doi.org/10.1007/978-3-319-45925-7_6
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