Text-to-speech inspired duration modeling for improved whole-word acoustic models

In the construction of whole-word acoustic models, we have previously demonstrated substantial gains by using MAP estimation to introduce a simple prior model of phonetic timing. Based solely on the word’s phonetic (dictionary) pronunciation, this simple model included no information about the individual durations of constituent phones. However, the problem of modeling segmental duration has long been studied in the textto-speech (TTS) community. We draw upon this work to develop a classification and regression tree (CART) approach for constructing prior models of phonetic timing which considers factors such as syllable stress, syllable position, adjacent phone class and voicing. This improved prior model closes 33% of the gap in keyword spotting performance between highly supervised whole-word models and those estimated without any examples.