Abstract
For the present work, we attempt to study the issue of automatic acquisition of intonational phrase breaks. A mathematically well-formed framework is suggested, which is based on Bayesian theory. Based on two different assumptions regarding the conditional independence of the input attributes, we have come up with two Bayesian implementations, namely the Naïve Bayes and the Bayesian Networks classifiers. As a performance benchmark, we evaluated the experimental result against CART, an acclaimed algorithm in the field of intonational phrase break detection that has demonstrated stat-of-the-art figures. Our approach utilizes minimal morphological and syntactic resources in a finite length window, i.e. the POS label and the type of syntactic phrase boundary, a novel attribute that has not been applied to the specific task before. On a 5500 word training set, the Bayesian networks approach proved to be the most effective, depicting precision and recall figures in the range of 82% and 77% respectively.
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References
Arvaniti, A., Baltazani, M.: GREEK ToBI: A System for the Annotation of Greek Speech Corpora. In: LREC 2000, vol. II, pp. 555–562 (2000)
Bachenco, J., Fitzpatrick, E.: A Computational grammar of Discourse-Neutral Prosodic Phrasing in English. Computational Linguistics 16(3), 155–170 (1990)
Bolinger, D.: Intonation and its Uses: Melody in Grammar and Discourse. Edward Arnold, London (1989)
Fordyce, C.S., Osterdorf, M.: Prosody Prediction for Speech Synthesis Using Transformational Rule-Based Learning. In: Proceedings of the International Conference on Spoken Language Processing (ICSLP 1998), pp. 682–685 (1998)
Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning 20, 197–243 (1995)
Hirschberg, J., Prieto, P.: Training Intonational Phrasing Rules Automatically for English and Spanish Text-to-Speech. Speech Communication 18, 281–290 (1996)
Lee, S., Oh, Y.H.: Tree-Based Modeling of Prosodic Phrasing and Segmental Duration for Korean TTS Systems. Speech Communication 28, 283–300
Mitchell, T.: Machine Learning. Mc Graw-Hill, New York (1997)
Muller, A.F., Zimmermann, H.G., Neuneier, R.: Robust Generation of Symbolic Prosody by a Neural Classifier Based on Autoassociators. In: Proceedings of the IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP 1996), pp. 1285–1288 (1996)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)
Damper, R.I. (ed.): Data-Driven techniques in Speech Synthesis, pp. 239–273. Kluwer Academic Publishers, Dordrecht (2001)
Sgarbas, K., Fakotakis, N., Kokkinakis, G.: A morphological description of MG using the twolevel model. In: Proceedings of the 19th Annual Workshop, Division of Linguistics, University of Thessaloniki, pp. 419–433 (1999)
Stamatatos, E., Fakotakis, N., Kokkinakis, G.A.: Practical Chunker for Unrestricted Text. In: Proceedings of the 2nd International Conference of Natural Language Processing, pp. 139–150 (2000)
Stone, M.: Cross-validation choice and assessment of statistical predictions. Journal of the Royal Statistical Society 36, 111–147 (1974)
Taylor, P., Black, A.W.: Assigning Phrase Breaks from Part-of-Speech Sequences. Computer Speech and Language 12, 99–117 (1998)
Wang, M.Q., Hirschberg, J.: Automatic Classification of Intonational Phrase Boundaries. Computer Speech and Language 6, 175–196 (1992)
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Maragoudakis, M., Zervas, P., Fakotakis, N., Kokkinakis, G. (2003). A Data-Driven Framework for Intonational Phrase Break Prediction. In: Matoušek, V., Mautner, P. (eds) Text, Speech and Dialogue. TSD 2003. Lecture Notes in Computer Science(), vol 2807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39398-6_27
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DOI: https://doi.org/10.1007/978-3-540-39398-6_27
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