Corpus-based dialogue systems rely on statistical models, whose parameters are inferred from annotated dialogues. The dialogues are usually annotated in terms of Dialogue Acts (DA), and the manual annotation is difficult (as annotation rule are hard to define), error-prone and time-consuming. Therefore, several semi-automatic annotation processes have been proposed to speed-up the process and consequently obtain a dialogue system in less total time. These processes are usually based on statistical models. The standard statistical annotation model is based on Hidden Markov Models (HMM). In this work, we explore the impact of different types of HMM, with different number of states, on annotation accuracy. We performed experiments using these models on two dialogue corpora (Dihana and SwitchBoard) of dissimilar features. The results show that some types of models improve standard HMM in a human-computer task-oriented dialogue corpus (Dihana corpus), but their impact is lower in a human-human non-task-oriented dialogue corpus (SwitchBoard corpus).

In the framework of the DIHANA project, we present the acquisitionprocess of a spontaneous speech dialogue corpus in Spanish. Theselected application consists of information retrieval by telephone for nationwide trains. A total of 900 dialogues from 225 users were acquired using the “Wizard of Oz” technique. In this work, we present the design and planning of the dialogue scenes and the wizard strategy used for the acquisition of the corpus. Then, we also present the acquisition tools and a description of the acquisition process.