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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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Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations., , , , , and . NODALIDA, page 281-290. Linköping University Electronic Press, (2019)Statistical Unpredictability of F0 Trajectories as a Cue to Sentence Stress., and . CogSci, cognitivesciencesociety.org, (2014)Prosodic Representations of Prominence Classification Neural Networks and Autoencoders Using Bottleneck Features., , , and . INTERSPEECH, page 1946-1950. ISCA, (2019)Attention based temporal filtering of sensory signals for data redundancy reduction., , and . ICASSP, page 3188-3192. IEEE, (2013)Perception of sentence stress in English infant directed speech., and . INTERSPEECH, page 1821-1825. ISCA, (2014)Speech-Based Emotion Recognition with Self-Supervised Models Using Attentive Channel-Wise Correlations and Label Smoothing., , , and . ICASSP, page 1-5. IEEE, (2023)Automatic detection of sentence prominence in speech using predictability of word-level acoustic features., and . INTERSPEECH, page 568-572. ISCA, (2015)Does the Importance of Word-Initial and Word-Final Information Differ in Native versus Non-Native Spoken-Word Recognition?, , , and . INTERSPEECH, page 858-862. ISCA, (2016)Evaluation of Spectral Tilt Measures for Sentence Prominence Under Different Noise Conditions., , and . INTERSPEECH, page 3211-3215. ISCA, (2017)3PRO - An unsupervised method for the automatic detection of sentence prominence in speech., and . Speech Commun., (2016)