Due to the large variability in the speech signal, the speech recognition process constitutes the major source of errors in most spoken dialogue systems. A spoken dialogue system can never know for certain what the user is saying, it can only make hypotheses. As a result of this uncertainty, two types of errors can be made: over-generation of hypotheses, which leads to misunderstanding, and under-generation, which leads to non-understanding. In human-human dialogue, speakers try to minimise such miscommunication by constantly sending and picking up signals about their understanding, a process commonly referred to as grounding.

The topic of this thesis is how to deal with this uncertainty in spoken dialogue systems: how to detect errors in speech recognition results, how to recover from non-understanding, how to choose when to engage in grounding, how to model the grounding process, how to realise grounding utterances and how to detect and repair misunderstandings. The approach in this thesis is to explore and draw lessons from human error handling, and to study how error handling may be performed in different parts of a complete spoken dialogue system. These studies are divided into three parts.

In the first part, an experimental setup is presented in which a speech recogniser is used to induce errors in human-human dialogues. The results show that, unlike the behaviour of most dialogue systems, humans tend to employ other strategies than encouraging the interlocutor to repeat when faced with non-understandings. The collected data is also used in a follow-up experiment to explore which factors humans may benefit from when detecting errors in speech recognition results. Two machine learning algorithms are also used for the task.

In the second part, the spoken dialogue system HIGGINS is presented, including the robust semantic interpreter PICKERING and the error aware discourse modeller GALATEA. It is shown how grounding is modelled and error handling is performed on the concept level. The system may choose to display its understanding of individual concepts, pose fragmentary clarification requests, or risk a misunderstanding and possibly detect and repair it at a later stage. An evaluation of the system with naive users indicates that the system performs well under error conditions.

In the third part, models for choosing when to engage in grounding and how to realise grounding utterances are presented. A decision-theoretic, data-driven model for making grounding decisions is applied to the data from the evaluation of the HIGGINS system. Finally, two experiments are presented, which explore how the intonation of synthesised fragmentary grounding utterances affect their pragmatic meaning.

The primary target of this thesis is the management of uncertainty, grounding and miscommunication in conversational dialogue systems, which to a larger extent build upon the principles of human conversation. However, many of the methods, models and results presented should also be applicable to dialogue systems in general.