This paper presents a statistical approach for automatic syllabification of words in Gujarati. Gujarati is a resource poor language and hardly any work for its syllabification has been reported, to the best our knowledge. Specifically, lack of enough training data makes this task difficult to perform. A training corpus of 14 thousand Gujarati words is built and a new approach to syllabification in Gujarati is tested on it. The maximum word and syllable level accuracies achieved are 91.89ï¾¿% and 98.02ï¾¿% respectively.
Gujarati,Training set,Computer science,Syllabification,Speech recognition,Natural language processing,Artificial intelligence,Syllable,Poor language