Prosody modelling in concept-to-speech generation: methodological issues

Abstract

Restricted accessMoreSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail Cite this article McKeown Kathleen R. and Pan Shimei 2000Prosody modelling in concept-to-speech generation: methodological issuesPhil. Trans. R. Soc. A.3581419–1431http://doi.org/10.1098/rsta.2000.0595SectionRestricted accessProsody modelling in concept-to-speech generation: methodological issues Kathleen R. McKeown Kathleen R. McKeown Department of Computer Science, Columbia University, New York, NY 10027, USA Google Scholar Find this author on PubMed Search for more papers by this author and Shimei Pan Shimei Pan Department of Computer Science, Columbia University, New York, NY 10027, USA Google Scholar Find this author on PubMed Search for more papers by this author Kathleen R. McKeown Kathleen R. McKeown Department of Computer Science, Columbia University, New York, NY 10027, USA Google Scholar Find this author on PubMed Search for more papers by this author and Shimei Pan Shimei Pan Department of Computer Science, Columbia University, New York, NY 10027, USA Google Scholar Find this author on PubMed Search for more papers by this author Published:15 April 2000https://doi.org/10.1098/rsta.2000.0595AbstractWe explore three issues for the development of concept–to–speech (CTS) systems. We identify information available in a language–generation system that has the potential to impact prosody; investigate the role played by different corpora in CTS prosody modelling; and explore different methodologies for learning how linguistic features impact prosody. Our major focus is on the comparison of two machine learning methodologies: generalized rule induction and memory–based learning. We describe this work in the context of multimedia abstract generation of intensive care (MAGIC) data, a system that produces multimedia briefings of the status of patients who have just undergone a bypass operation. Previous Article VIEW FULL TEXT DOWNLOAD PDF FiguresRelatedReferencesDetailsCited by Jordan D and Rose S (2009) Multimedia Abstract Generation of Intensive Care Data: The Automation of Clinical Processes Through AI Methodologies, World Journal of Surgery, 10.1007/s00268-009-0319-5, 34:4, (637-645), Online publication date: 1-Apr-2010. Theune M, Heylen D and Nijholt A (2005) Generating Embodied Information Presentations Multimodal Intelligent Information Presentation, 10.1007/1-4020-3051-7_3, (47-67), . Xydas G, Spiliotopoulos D and Kouroupetroglou G (2004) Modeling Prosodic Structures in Linguistically Enriched Environments Text, Speech and Dialogue, 10.1007/978-3-540-30120-2_66, (521-528), . Bulyko I and Ostendorf M (2002) Efficient integrated response generation from multiple targets using weighted finite state transducers, Computer Speech & Language, 10.1016/S0885-2308(02)00023-2, 16:3-4, (533-550), Online publication date: 1-Jul-2002. Jordan D, McKeown K, Concepcion K, Feiner S and Hatzivassiloglou V (2001) Generation and Evaluation of Intraoperative Inferences for Automated Health Care Briefings on Patient Status After Bypass Surgery, Journal of the American Medical Informatics Association, 10.1136/jamia.2001.0080267, 8:3, (267-280), Online publication date: 1-May-2001. This Issue15 April 2000Volume 358Issue 1769Discussion Meeting Issue ‘Computers, language and speech: formal theories and statistical data’ organized by the Royal Society and the British Academy Article InformationDOI:https://doi.org/10.1098/rsta.2000.0595Published by:Royal SocietyPrint ISSN:1364-503XOnline ISSN:1471-2962History: Published online15/04/2000Published in print15/04/2000 License: Citations and impact KeywordsConcept–To–Speech Generation Speech Synthesis Natural Language Generation Machine Learning