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Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations.

, , , , , and . NODALIDA, page 281-290. Linköping University Electronic Press, (2019)

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Utilizing glottal source pulse library for generating improved excitation signal for HMM-based speech synthesis., , , , and . ICASSP, page 4564-4567. IEEE, (2011)Prosodic and syntactic segmentation of spontaneous speech: A preliminary study., , , and . ICPhS, University of Glasgow, (2015)Voice source modelling using deep neural networks for statistical parametric speech synthesis., , , , , , and . EUSIPCO, page 2290-2294. IEEE, (2014)Phase perception of the glottal excitation of vocoded speech., , , , and . INTERSPEECH, page 254-258. ISCA, (2015)New method for delexicalization and its application to prosodic tagging for text-to-speech synthesis., , , , , and . INTERSPEECH, page 1703-1706. ISCA, (2009)Deep neural network based trainable voice source model for synthesis of speech with varying vocal effort., , , , and . INTERSPEECH, page 1969-1973. ISCA, (2014)Lombard modified text-to-speech synthesis for improved intelligibility: submission for the hurricane challenge 2013., , , , , and . INTERSPEECH, page 3562-3566. ISCA, (2013)Comparative Analysis of Prosodic Characteristics Using WaveNet Embeddings., , , and . INTERSPEECH, page 2538-2542. ISCA, (2019)Deep Syntactic Analysis and Rule Based Accentuation in Text-to-Speech Synthesis., and . TSD, volume 5246 of Lecture Notes in Computer Science, page 535-542. Springer, (2008)Prosodic Representations of Prominence Classification Neural Networks and Autoencoders Using Bottleneck Features., , , and . INTERSPEECH, page 1946-1950. ISCA, (2019)