Dialogue context sensitive speech synthesis using factorized decision trees
Pirros Tsiakoulis, Catherine J. Breslin, M. Gašić, Matthew Henderson, Dongho Kim, Steve Young · 2014
This paper extends our recent work on rich context utilization for expressive speech synthesis in spoken dialogue systems in which significant improvements to the appropriateness of HMM-based synthetic voices were achieved by introducing dialogue context into the decision tree state clustering stage. Continuing in this direction, this paper investigates the performance of dialogue context-sensitive voices in different domains. The Context Adaptive Training with Factorized Decision trees (FD-CAT) approach was used to train a dialogue context-sensitive synthetic voice which was then compared to a baseline system using the standard decision tree approach. Preference-based listening tests were conducted for two different domains. The first domain concerned restaurant information and had significant coverage in the training data, while the second dealing with appointment bookings had minimal coverage in the training data. No significant preference was found for any of the voices when tested in the restaurant domain whereas in the appointment booking domain, listeners showed a statistically significant preference for the adaptively trained voice. Index Terms: HMM-based expressive speech synthesis, dia-logue context-sensitive speech synthesis, context adaptive train-ing, factorized fecision trees 1.