Abstract
Text analysis and, better still, text understanding are some of the biggest challenges taken up by artificial intelligence. Over the last thirty-five years, computational linguistics specialists have proposed an impressive number of linguistic formalisms and inference methods to tackle these problems. Is it not surprising, under these conditions, that few of the speech synthesis systems commercialized up to now embody a somewhat complete parser to properly examine the sentences to pronounce? And how do we explain the desperate eagerness of specialists to obtain a reasonable synthetic speech quality with just some simple syntactic heuristics, while research teams in natural language processing (NLP) now focus on the much more complex task of analyzing semantics and pragmatics in the context of automatic understanding, translation, or production of natural language?
La grammaire est l’art de lever les ambiguités de la langue; mais il nefautpas que le levier soit plus lourd quelefardeau. (Grammar is the art of removing the ambiguities of a language; but the lever should not be heavier than the burden). A. Rivarol, (1784), De l’Universalité de la Langue
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© 1997 Springer Science+Business Media Dordrecht
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Dutoit, T. (1997). NLP Architectures for TTS Synthesis. In: An Introduction to Text-to-Speech Synthesis. Text, Speech and Language Technology, vol 3. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5730-8_3
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DOI: https://doi.org/10.1007/978-94-011-5730-8_3
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