Modeling Pronunciation Variation for Cantonese Speech Recognition

Due to the large variability of pronunciation in spontaneous speech, pronunciation modeling becomes a more challenging and essential part in speech recognition. In this paper, we describe two different approaches of pronunciation modeling by using decision tree. At lexical level, a pronunciation variation dictionary is built to obtain alternative pronunciations for each word, in which each entry is associated with a variation probability. At decoding level, decision tree pronunciation models are applied to expand the search space to include alternative pronunciations. Relative error reduction of 7.21% and 4.81% could be achieved at lexical level and decoding level respectively. The results at the two different levels are compared and contrasted.