Segment modeling alternatives for continuous speech recognition
Owen Kimball, Mari Ostendorf · 1995
This dissertation presents alternative parametric statistical models of phoneticallybased segments for use in continuous speech recognition (CSR). A categorization of segment modeling approaches is proposed according to two characteristics: the assumed form of the probability distribution and the representation chosen for segment observations. The question of distribution form divides models into two groups: those based on conditional probability densities of feature given label and those using a posteriori probabilities of label given feature. The second characteristic concerns whether a model uses a variable or fixed-length representation of observed speech segments. The choices for both characteristics have important implications, particularly for context modeling and score normalization. In this work, specific segment models are developed in order to understand the benefits and limitations that follow from these choices.