Poor speech recognition is a problem when developing spoken dialogue systems, but
several studies has showed that speech recognition can be improved by post-processing
of recognition output that use the dialogue context, acoustic properties of a user utterance
and other available resources to train a statistical model to use as a filter between
the speech recogniser and dialogue manager. In this thesis a corpus of logged interactions
between users and a dialogue system was used to extract features from previous
dialogue context, acoustics from the user utterance and n-best recognition hypotheses.
The features were used to train maximum entropy models with different feature sets
to rerank the n-best hypotheses. The models fail to some extent to predict intended
labels but using the reranked output in effect means that 94.9% of the adequate hypotheses
will be sent to the dialogue manager, a decrease in relative error over baseline with
44.6% showing that contextual reranking can improve speech recognition for dialogue
systems. Future work involves developing the current feature sets and maxEnt models
to better classify whether a hypothesis should be accepted or rejected by the dialogue
system rather than rerank them
