Simulated spoken dialogue system based on IOHMM with user history

Abstract

Expanding corpora is very important in designing a spoken dialogue system (SDS). In this big data era, data is expensive to collect and there are rare annotations. Some researchers make much work to expand corpora, most of which is based on rule. This paper presents a probabilistic method to simulate dialogues between human and machine so as to expand a small corpus with more varied simulated dialogue acts. The method employs Input/output HMM with user history (UH-IOHMM) to learn system and user dialogue behavior. In addition, this paper compares with simulation system based on standard IOHMM. We perform experiments using the WDC-ICA corpus, weather domain corpus with annotation. And the experiment result shows that the method we present in this paper can produce high quality dialogue acts which are similar to real dialogue acts. © Springer-Verlag Berlin Heidelberg 2013.