Abstract
In this paper, we present a new approach for classification of phrase types. It is based on utilization of context-dependent hidden Markov models that consider the phonetic, prosodic and linguistic context. The classification is performed by forced-alignment for particular phrase types and selection of the type with the best alignment score. Experiments were performed on 2 large speech corpora. The classification results were successfully verified by a listening test. The speech corpora with corrected prosodemes were used in a unit selection speech synthesis framework. Another listening test confirmed that the prosody of particular phrases improved in comparison with the baseline system.
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Hanzlíček, Z. (2015). Classification of Prosodic Phrases by Using HMMs. In: Král, P., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2015. Lecture Notes in Computer Science(), vol 9302. Springer, Cham. https://doi.org/10.1007/978-3-319-24033-6_56
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DOI: https://doi.org/10.1007/978-3-319-24033-6_56
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