This paper introduces an engineering-oriented approach towards dialogue modelling. While dialogue models in existing dialogue systems usually are manually coded, or at least the data on which they are based is manually labeled, we investigate the possibility of learning dialogue models from a large set of example dialogues using real data in a spoken dialogue system. We assume the dialogue syst...

Current spoken dialogue systems lack positive feedback such as backchannels, which are common in human-human conversations. To develop more natural human-computer interfaces, the investigation of backchannel-responses are indispensable. In this paper, we propose a method for detecting the precise timing for backchannel responses in Japanese and aim at incorporating such method in future spoken ...

Recent work in designing spoken dialogue systems has focused on using Reinforcement Learning to automatically learn the best action for a system to take at any point in the dialogue to maximize dialogue success. While policy development is very important, choosing the best features to model the user state is equally important since it impacts the actions a system should make. In this paper, we ...

We describe a two-step approach for dialogue management in task-oriented spoken dialogue systems. A unified neural network framework is proposed to enable the system to first learn by supervision from a set of dialogue data and then continuously improve its behaviour via reinforcement learning, all using gradientbased algorithms on one single model. The experiments demonstrate the supervised mo...

This overview article reviews the structure of a fully statistical spoken dialogue system (SDS), using as illustration, various systems and components built at Cambridge over the last few years. Most of the components in an SDS are essentially classifiers which can be trained using supervised learning. However, the dialogue management component must track the state of the dialogue and optimise ...

In the past decade, goal-oriented spoken dialogue systems have been the most prominent component in today’s virtual personal assistants. The classic dialogue systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applications of neural models to dialogue modeling. However, how to successfully apply deep learning based approaches to...

In an object manipulation dialogue, a robot may misunderstand an ambiguous command from a user, such as “Place the cup down (on the table), ” potentially resulting in an accident. Although making confirmation questions before all motion execution will decrease the risk of this failure, the user will find it more convenient if confirmation questions are not made under trivial situations. This pa...

Spoken dialogue researchers often use supervised machine learning to classify turn-level user affect from a set of turn-level features. The utility of sub-turn features has been less explored, due to the complications introduced by associating a variable number of sub-turn units with a single turn-level classification. We present and evaluate several voting methods for using wordlevel pitch and...

This study analyzes the acoustic-prosodic features observed in spoken corrections in a task-oriented spoken dialogue. While previous studies have been found that spoken corrections are often predicted as hyperarticulate speech and related to some acousticprosodic events, their close analysis were not shown in terms of various dialogue situations in practical spoken dialogue systems and tasks. I...

Spoken language dialogue systems represent the peak of achievement in speech technologies in the 20 th century and appear set to form the basis for the increasingly natural interactive systems to follow in the coming decades. This chapter first presents a model of the task-oriented spoken dialogue system, its multiple aspects and some of the remaining research challenges. In the context of this...