are often misunderstood due to hyper articulation  Levow s experiments train a decision tree using features such as duration tempo pitch amplitude and within utterance pauses Examination of the trained tree in this study also reveals that the durational features are the most discriminatory Similarly in our experiments RIPPER uses asr-duration frequently in the developed rule set Levow obtains an accuracy rate of with a baseline of and  Hirschberg et al b  perform similar studies for automat Swerts et al ically identifying corrections using prosody ASR features and dialogue context  Corrections are likely to be misrecognized  due to hyperarticulation   They observe that corrections that are more distant from the error they correct  are more likely to exhibit prosodic differences Their system automatically differentiates corrections from non corrections with an error Dialogue context is used in the study by  Hirschberg  Litman rate of Swerts whereby they incorporate whether the user is aware of a mistake at the current a utterance to help predict misunderstandings and misrecognition of the previous utterances This study is similar to ours in that they use a predicted feature about an utterance the aware feature to predict concept or word accuracy as we use a predicted feature auto¬
SLU-success in the PDP  However  our auto-SLU-success feature is automatically available at the time the prediction is being made  whereas they are making the predictions retroac tively In addition they train their system on the hand labelled feature rather than the predicted one which they leave as further work performs error correction identification using task independent acous Kirch hoff tic and discourse variables This is a two way distinction between positive and negative error correction She uses two cascaded classifiers the first is a decision tree trained using of the data and validating on Examples that have confidence scores below a threshold go into an exception training set for a second classifier  During testing  if confi dence scores are below a threshold then the utterance is passed onto the second classifier She finds that the most discriminatory features are dialogue context  the type of previous ssssem utterance  followed by lexical features  with prosodic features being the least dis criminatory The system recognizes error corrections with an accuracy of cmpared to a baseline of In this study  Kirchhoff deliberately eschews the use of sys tem specific features  while in our work  we examine the separate contribution of different feature sets Our results suggest that the use of more general features does not negatively impact performance Krahmer Swerts Theune Weegels a and Krahmer Swerts Theune b look at different features related to responses to problematic system turns Weegels The disconfirmations they discuss are responses to explicit or implicit system verification questions  They observe that disconfirmations are longer  have a marked word order  and contain specific lexicon such as   no"  In addition  there are specific prosodic cues such as boundary tones and pauses  Some of these features such as length choice of words are captured in our RIPPER ruleset as discussed above As described in Section two methodologies were compared for incorporating the fea ture SLU-success into the PDP The first was to use the hand labelled feature in the training set the second to perform separate experiments to predict the feature for the training set As the features in the training set are automatically predicted  it is hoped that the system would pick up the idiosyncrasies of the noisy data  This training method has been used pre viously in  Wright where automatically identified intonation event features are used