Conclusion and Future Research Directions

Abstract

Processing of speech and language inputs has been studied under a statistical point of view for quite some time. Data-driven methods pioneered by speech recognition researchers such as Rabiner [8] and Jelinek [2] in the late 1970s were applied to natural language understanding only in the early 1990s [5]. It is only a decade ago that dialogue management has benefited from statistical modeling and data-driven methods [3]. Following this trend, this book described the recent advances in statistical data-driven methods for spoken dialogue systems, especially within the European CLASSiC project funded under the seventh framework program. The aim of this project, as reflected by this book, was to produce generic methods for statistical optimization from end to end of a spoken dialogue system, starting with speech recognition and ending with speech synthesis. Machine learning techniques, such as reinforcement learning, were expected to provide useful approaches to this problem because of their ability to solve sequential decision-making problems but also because they rely on a strong mathematical background and on interpretable optimization criteria. Reinforcement learning has thus been applied for optimizing dialogue management and natural language generation (NLG) but also, to some extent, to produce user simulation techniques. Other machine learning methods, such as support vector machines (SVM) or Bayesian networks, were also integrated to make a fully operational system. This book summarized the technical and practical results obtained during this project.