Adapting phonetic decision trees between languages for continuous speech recognition

Abstract

In a continuous speech recognition system it is important to model the context dependent variations in the pronunciations of phones. In this work we have attempted to build decision trees for modeling phonetic context-dependency in Hindi. The approach followed is to modify a decision tree built to model context-dependency in American English. The reason the decision trees turn out to be different are that the English and Hindi phoneme sets are not identical. Then even for identical phonemes, the context-dependency is different for the two languages. Linguistic-Phonetic knowledge of Hindi is used to modify the English phone set. Since the Hindi phone set being used is derived from the English phone set, the adaptation of the English tree to Hindi follows naturally. Though here the adaptation is from English to Hindi, the method may be applicable for adapting between any two languages. The decision tree is built using either Hindi data or English data labeled with the correct Hindi contexts. This procedure is discussed and the limitations of both the methods are described.