  We present a framework for the induction of semantic frames from utterances
in the context of an adaptive command-and-control interface. The system is
trained on an individual user's utterances and the corresponding semantic
frames representing controls. During training, no prior information on the
alignment between utterance segments and frame slots and values is available.
In addition, semantic frames in the training data can contain information that
is not expressed in the utterances. To tackle this weakly supervised
classification task, we propose a framework based on Hidden Markov Models
(HMMs). Structural modifications, resulting in a hierarchical HMM, and an
extension called expression sharing are introduced to minimize the amount of
training time and effort required for the user.
  The dataset used for the present study is PATCOR, which contains commands
uttered in the context of a vocally guided card game, Patience. Experiments
were carried out on orthographic and phonetic transcriptions of commands,
segmented on different levels of n-gram granularity. The experimental results
show positive effects of all the studied system extensions, with some effect
differences between the different input representations. Moreover, evaluation
experiments on held-out data with the optimal system configuration show that
the extended system is able to achieve high accuracies with relatively small
amounts of training data.
