Personalised Dialogue Management for Users with Speech Disorders

Many electronic devices are beginning to include Voice User Interfaces (VUIs) as an alternative to conventional interfaces. VUIs are especially useful for users with restricted upper limb mobility, because they cannot use keyboards and mice. These users, however, often suffer from speech disorders (e.g. dysarthria), making Automatic Speech Recognition (ASR) challenging, thus degrading the performance of the VUI. Partially Observable Markov Decision Process (POMDP) based Dialogue Management (DM) has been shown to improve the interaction performance in challenging ASR environments, but most of the research in this area has focused on Spoken Dialogue Systems (SDSs) developed to provide information, where the users interact with the system only a few times. In contrast, most VUIs are likely to be used by a single speaker over a long period of time, but very little research has been carried out on adaptation of DM models to specific speakers. This thesis explores methods to adapt DM models (in particular dialogue state tracking models and policy models) to a specific user during a longitudinal interaction. The main differences between personalised VUIs and typical SDSs are identified and studied. Then, state-of-the-art DM models are modified to be used in scenarios which are unique to long-term personalised VUIs, such as personalised models initialised with data from different speakers or scenarios where the dialogue environment (e.g. the ASR) changes over time. In addition, several speaker and environment related features are shown to be useful to improve the interaction performance. This study is done in the context of homeService, a VUI developed to help users with dysarthria to control their home devices. The study shows that personalisation of the POMDP-DM framework can greatly improve the performance of these interfaces