The effect of language model probability on pronunciation reduction

Abstract

We investigate how the probability of a word affects its pronunciation. We examined 5618 tokens of the 10 most frequent (function) words in Switchboard and 2042 tokens of content words whose lexical form ends in a t or d. Our observations were drawn from the phonetically hand-transcribed subset of the Switchboard corpus, enabling us to code each word with its pronunciation and duration. Using linear and logistic regression to control for contextual factors, we show that words which have a high unigram, bigram, or reverse bigram (given the following word) probability are shorter, more likely to have a reduced vowel, and more likely to have a deleted final t or d. These results suggest that pronunciation models in speech recognition and synthesis should take into account word probability given both the previous and following words, for both content and function words.