Automatic learning of numeral grammars for multi-lingual speech synthesizers

Presented is a trainable data-driven method of deriving numerals from number strings. The concept is based on learning a graph model for numeral grammars and a graph search which is capable of extracting numeral words from the grammar according to given number strings. Because this method separates code and data it is universal and applicable to every language. No a priori knowledge about formal descriptions of numeral grammars is required. Due to the underlying graph concept, the algorithm is able to automatically generate grammars for number to numeral translations with a minimal effort. The only required input information is a set of pairs of number strings and appropriate numerals. This method is useful for an easy implementation of knowledge bases for further languages in the framework of a multi-lingual speech synthesis system