Spoken control interfaces are very attractive to people with severe physical disabilities who often also have a type of speech disorder known as dysarthria. This condition is known to decrease the accuracy of automatic speech recognisers (ASRs) especially for users with moderate to severe dysathria. In this paper we investigate how applying probabilistic dialogue management (DM) techniques can improve interaction performance of an environmental control system for such users. The effect of having access to different amounts of adaptation data, as well as using different vocabulary size for speakers of different intelligibilities is investigated. We explore the effect of adapting the DM models as the ASR performance increases, such as is the case in systems where more adaptation data is collected through system use. Improvements compared to a non-probabilistic DM baseline are seen both in terms of dialogue length and success rate, 9% and 25% mean relative improvement respectively. Looking at just the more severe dysarthric speakers these numbers rise 25% and 75% mean relative improvement. These improvements are higher when the ASR data adaptation amount is small. Further results show that a DM trained on data from multiple speakers outperform a DM trained on data from a single speaker.
Bibliographic reference. Casanueva, I. / Christensen, H. / Hain, Thomas / Green, Phil D. (2014): "Adaptive speech recognition and dialogue management for users with speech disorders", In INTERSPEECH-2014, 1033-1037.