Machine Learning Methods for Spoken Dialogue Simulation and Optimization

Computers and electronic devices are becoming more and more present in our day-to-day life. This can of course be partly explained by their ability to ease the achievement of complex and boring tasks, the important decrease of prices or the new entertainment styles they offer. Yet, this real incursion in everybody's life would not have been possible without an important improvement of Human-Computer Interfaces (HCI). This is why HCI are now widely studied and become a major trend of research among the scientific community. Designing “user-friendly” interfaces usually requires multidisciplinary skills in fields such as computer science, ergonomics, psychology, signal processing etc. In this chapter, we argue that machine learning methods can help in designing efficient speech-based humancomputer interfaces

Learning to ground in spoken dialogue systems

PosterMachine learning methods such as reinforcement learning applied to dialogue strategy optimization has become a leading subject of researches since the mid 90's. Indeed, the great variability of factors to take into account makes the design of a spoken dialogue system a tailoring task and reusability of previous work is very difficult. Yet, techniques such as reinforcement learning are very demanding in training data while obtaining a substantial amount of data in the particular case of spoken dialogues is time-consuming and therefore expansive. In order to expand existing data sets, dialogue simulation techniques are becoming a standard solution. In this paper, we present a user model for realistic spoken dialogue simulation and a method for using this model so as to simulate the grounding process. This allows including grounding subdialogues as actions in the reinforcement learning process and learning adapted strateg