Publication at Faculty of Mathematics and Physics |
2014
Abstract
This paper describes a factored discriminative spoken language understanding method suitable for real-time parsing of recognised speech. It is based on a set of logistic regression classifiers, which are used to map input utterances into dialogue acts.
The proposed method is evaluated on a corpus of spoken utterances from the Public Transport Information (PTI) domain. In PTI, users can interact with a dialogue system on the phone to find intra- and inter-city public transport connections and ask for weather forecast in a desired city.
The results show that in adverse speech recognition conditions, the statistical parser yields significantly better results compared to the baseline well-tuned handcrafted parser.