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Up to what level can acoustical and textual features predict prominence.

, , and . INTERSPEECH, page 811-814. ISCA, (2001)

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Acoustic-phonetic and artificial neural network feature analysis to assess speech quality of stop consonants produced by patients treated for oral or oropharyngeal cancer., , , , , , and . Speech Commun., 54 (5): 632-640 (2012)DIANA, an end-to-end computational model of human word comprehension., , and . ICPhS, University of Glasgow, (2015)A novel feature transformation for vocal tract length normalization in automatic speech recognition., , , and . IEEE Trans. Speech Audio Process., 6 (6): 549-557 (1998)Unconstrained Speech Segmentation using Deep Neural Networks., , and . ICPRAM (1), page 248-254. SciTePress, (2015)Spoken Conversational Agents for Older Adults: Who Are the Stakeholders and What Do They Expect?, , , , , , and . IUI Workshops, volume 2903 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)Phonetic-acoustic and feature analyses by a neural network to assess speech quality in patients treated for head and neck cancer., , , , , , , and . INTERSPEECH, page 1753-1756. ISCA, (2008)Comparing EEG Analyses with Different Epoch Alignments in an Auditory Lexical Decision Experiment., , and . INTERSPEECH, page 130-134. ISCA, (2020)On the error criteria in neural networks as a tool for human classification modelling., and . ICSLP, page 510-513. ISCA, (1996)Analysis of context-dependent segmental duration for automatic speech recognition., , and . ICSLP, page 1181-1184. ISCA, (1996)Analytical Assessment of Dual-Stream Merging for Noise-Robust ASR., , and . INTERSPEECH, page 3793-3797. ISCA, (2016)