Abstract
In this paper we present a system for automatically predicting prosodic breaks in synthesized speech using the Random Forests classifier. In our experiments the classifier is trained on a large dataset consisting of audiobooks, which is automatically labeled with phone, word, and pause labels. To provide part of speech (POS) tags in the text, a rule-based POS tagger is used. We use crossvalidation in order to be able to examine not only the results for a specific subset of data but also the systems reliability across the dataset. The experimental results demonstrate that the system shows good and consistent results on the audiobook database; the results are poorer and less robust on a smaller database of read speech even though part of that database was labeled manually.
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Khomitsevich, O., Chistikov, P., Zakharov, D. (2014). Using Random Forests for Prosodic Break Prediction Based on Automatic Speech Labeling. In: Ronzhin, A., Potapova, R., Delic, V. (eds) Speech and Computer. SPECOM 2014. Lecture Notes in Computer Science(), vol 8773. Springer, Cham. https://doi.org/10.1007/978-3-319-11581-8_58
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DOI: https://doi.org/10.1007/978-3-319-11581-8_58
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