Interaction timing in conversation exhibits myriad variabilities, yet it is patently not random. However, identifying consistencies is a manually labor-intensive effort, and findings have been limited. We propose a conditonal mutual information measure of the influence of prosodic features, which can be computed for any conversation at any instant, with only a speech/non-speech segmentation as its requirement. We evaluate the methodology on two segmental features: energy and speaking rate. Results indicate that energy, the less controversial of the two, is in fact better on average at predicting conversational structure. We also explore the temporal evolution of model 'surprise', which permits identifying instants where each feature's influence is operative. The method corroborates earlier findings, and appears capable of large-scale data-driven discovery in future research.

In this paper, we present a study where a robot instructs a human on how to draw a route on a map. The human and robot are seated face-to-face with the map placed on the table between them. The user's and the robot's gaze can thus serve several simultaneous functions: as cues to joint attention, turn-taking, level of understanding and task progression. We have compared this face-to-face setting with a setting where the robot employs a random gaze behaviour, as well as a voice-only setting where the robot is hidden behind a paper board. In addition to this, we have also manipulated turn-taking cues such as completeness and filled pauses in the robot's speech. By analysing the participants' subjective rating, task completion, verbal responses, gaze behaviour, and drawing activity, we show that the users indeed benefit from the robot's gaze when talking about landmarks, and that the robot's verbal and gaze behaviour has a strong effect on the users' turn-taking behaviour. We also present an analysis of the users' gaze and lexical and prosodic realisation of feedback after the robot instructions, and show that these cues reveal whether the user has yet executed the previous instruction, as well as the user's level of uncertainty.

In this paper, we present a user study where a robot instructs a human on how to draw a route on a map, similar to a Map Task. This setup has allowed us to study user reactions to the robot’s conversational behaviour in order to get a better understanding of how to generate utterances in incremental dialogue systems. We have analysed the participants' subjective rating, task completion, verbal responses, gaze behaviour, drawing activity, and cognitive load. The results show that users utilise the robot’s gaze in order to disambiguate referring expressions and manage the flow of the interaction. Furthermore, we show that the user’s behaviour is affected by how pauses are realised in the robot’s speech.

Questions and answers play an important role in spoken dialogue systems as well as in human-human interaction. A critical concern when responding to a question is the timing of the response. While human response times depend on a wide set of features, dialogue systems generally respond as soon as they can, that is, when the end of the question has been detected and the response is ready to be deployed. This paper presents an analysis of how different semantic and pragmatic features affect the response times to questions in two different data sets of spontaneous human-human dialogues: the Swedish Spontal Corpus and the US English Switchboard corpus. Our analysis shows that contextual features such as question type, response type, and conversation topic influence human response times. Based on these results, we propose that more sophisticated response timing can be achieved in spoken dialogue systems by using these features to automatically and deliberately target system response timing.

This paper presents a model of incremental speech generation in practical conversational systems. The model allows a conversational system to incrementally interpret spoken input, while simultaneously planning, realising and self-monitoring the system response. If these processes are time consuming and result in a response delay, the system can automatically produce hesitations to retain the floor. While speaking, the system utilises hidden and overt self-corrections to accommodate revisions in the system. The model has been implemented in a general dialogue system framework. Using this framework, we have implemented a conversational game application. A Wizard-of-Oz experiment is presented, where the automatic speech recognizer is replaced by a Wizard who transcribes the spoken input. In this setting, the incremental model allows the system to start speaking while the user's utterance is being transcribed. In comparison to a non-incremental version of the same system, the incremental version has a shorter response time and is perceived as more efficient by the users.

This paper investigates forms and functions of user feedback in a map task dialogue between a human and a robot, where the robot is the instruction-giver and the human is the instruction- follower. First, we investigate how user acknowledgements in task-oriented dialogue signal whether an activity is about to be initiated or has been completed. The parameters analysed include the users' lexical and prosodic realisation as well as gaze direction and response timing. Second, we investigate the relation between these parameters and the perception of uncertainty.

We present an attempt at using 3rd party observer gaze to get a measure of how appropriate each segment in a dialogue is for a speaker change. The method is a step away from the current dependency of speaker turns or talkspurts towards a more general view of speaker changes. We show that 3rd party observers do indeed largely look at the same thing (the speaker), and how this can be captured and utilized to provide insights into human communication. In addition, the results also suggest that there might be differences in the distribution of 3rd party observer gaze depending on how information-rich an utterance is.

This paper describes a study of how the gazes of 3rd party observers of dialogue move when a speaker is taking the turn and producing a back-channel, respectively. The data is collected and basic processing is complete, but the results section for the paper is not yet in place. It will be in time for the workshop, however, and will be presented there, should this paper outline be accepted..