In conversational dialogue applications it is
critical to understand the requests accurately.
However, the performance of current speech
recognition systems are far from perfect. In order to
function effectively with imperfect speech
recognition, an accurate confidence scoring mechanism
should be employed. To determine a confidence score
for a hypothesis, certain confidence features are
combined. In this work, the performance of
filler-model based confidence features are
investigated. Five types of filler model are defined:
triphone-network, phone-network, phone-class network,
5-state...