Context free wild card parsing in a text-to-speech system

In text-to-speech systems, syntactic knowledge is necessary for correct grapheme-to-phoneme conversion, and calculation of prosody. Markov models can assign the correct word classes needed for grapheme-to-phoneme conversion in most of the cases, but only a small part of the information needed for correct prosody. Conventional syntactic parsers almost never produce unique output: they either over-generate or fail to yield a syntactic analysis. Moreover, they cannot handle input that is not syntactically correct, and they are slow and expensive. Wild card parsing (WCP) can overcome these problems and provide the syntactic information needed for prosody calculations in unrestricted tests. The WCP system is implemented in a language-independent way. All language-dependent knowledge is kept strictly separated from the program. The WCP system has been integrated into a multilingual text-to-speech system where it provides syntactic information for the generation of prosodic contours for any input sentence. As a by-product of the syntactic parsing it disambiguates the word classes of the words in the sentence, thus helping grapheme-to-phoneme conversion