| Description: |
Spoken dialogue systems (SDSs, e.g. Siri and Alexa) are trained on huge corpora, helping them accurately understand the ‘average’ user. Speech production is nuanced, however, so some user groups fall outside the ‘average’. This thesis focuses on SDSs for people with dementia (PwD). More naturally interactive and accessible SDSs can improve people’s autonomy at home, and in public spaces. Three challenges are tackled in this thesis, ethical data collection, incrementality, and multi-party conversations (MPCs). Part I details the motivations of this work, in the context of voice assistant accessibility, with a specific focus on language technologies for people with dementia. The thesis is then introduced in its entirety through published paper summaries, with a structure guide. Part II focuses on data collection. An ethical framework is presented to ensure all data is collected ethically. A data capture device is then presented to address novel challenges introduced by COVID-19. Using the ethical framework and device, the DEICTIC corpus was collected. It verified that, when talking to an SDS, PwD pause significantly more often, and for significantly longer durations, than people without dementia. The corpus also reveals that 28% of PwD’s interactions with an SDS are MPCs involving their partner. SDSs are not adapted for MPCs, so a second data collection was designed. Hospital staff subsequently used this design with memory clinic patients and their companions. Part III focuses on incrementality. Microsoft’s incremental speech recognition is the most responsive, stable, accurate, and the only one that preserves disfluent material. IBM’s services were the most suitable for MPCs. Two corpora were created and released to explore incremental semantic parsing, together containing over 105,000 interrupted utterances paired with their underspecified meaning representation. SDSs interrupt users if they pause too long mid-utterance, requiring them to frustratingly repeat themselves. The use of incremental clarification ... |