Machine Learning algorithms for linguistic aspects of speech synthesis

  • Busser, G.J., (Researcher)

    Project: Research project

    Project Details

    Description

    The goal of this project is the data-oriented construction of a computer model of the linguistic knowledge and processes relevant for text to speech conversion. Especially the determination of lexical stress, prosodic phrasing of sentences, and the determination of sentence accents are studied. The method we propose uses inductive artificial learning algorithms. A second goal of this project is to acquire a better insight into how and in what proportion information sources, which are claimed to be necessary in the literature, contribute effectively to a naturally sounding intonation, thereby gaining linguistic relevancy.
    StatusFinished
    Effective start/end date1/01/971/01/01

    Research Output

    • 1 Chapter
    • 1 Conference contribution
    • 1 Article

    Designing an active learning based system for corpus annotation

    Busser, G. J. & Morante, R., 2005, In : Procesamiento del Lenguaje natural, Revista. Sept. 2005, 35, p. 375-382 8 p.

    Research output: Contribution to journalArticleScientificpeer-review

    Machine learning of word pronunciation: the case against abstraction

    Busser, G. J., Daelemans, W. & van den Bosch, A., 1999, Proceedings of the Sixth European Conference on Speech Communication and Technology, Eurospeech 99, Budapest, Hungary, september 5-10, 1999. Olaszy, G., Nemeth, G. & Erdohegyi, K. (eds.). Bonn, Germany: EXCA, p. 2123-2127 5 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    TreeTalk-D: a Machine Learning Approach to Dutch Word Pronunciation

    Busser, G. J., 1998, Proceedings of the Text, Speech and Dialog Conference 1998. Sojka, P., Matousek, V., Pala, K. & Kopecek, I. (eds.). Brno (Cz): Masaryk University, p. 3-8 446 p.

    Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review