Abstract
This chapter introduces a simulator for incremental human-machine dialogue in order to generate artificial dialogue datasets that can be used to train and test data-driven methods. We review the various simulator components in detail, including an unstable speech recognizer, and their differences with non-incremental approaches. Then, as an illustration of its capacities, an incremental strategy based on hand-crafted rules is implemented and compared to several non-incremental baselines. Their performances in terms of dialogue efficiency are presented under different noise conditions and prove that the simulator is able to handle several configurations which are representative of real usages.
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Notes
- 1.
This rule is kept as an indication, it is the French way of telling time and it does not apply to English.
- 2.
Here, the value of the priority variable designate its importance. Therefore, the higher the priority, the more important the task is.
- 3.
We do not claim that the Intent Manager algorithm solves the task in an optimal way. Moreover, there are some pathological examples that are not handled at all. Yet it complies with our objective to have a simple algorithm able to run realistic dialogues to study turn-taking mechanisms.
- 4.
This N-Best corresponds to the last input word only. It is important to make the distinction between this N-Best and the one corresponding to the last partial utterance as a whole. In Fig. 3, the block New word N-Best is a word N-Best whereas the other three blocks are partial utterances N-Best.
- 5.
Here, we only use the best hypothesis of the N-Best. However, the others are indirectly used through the boost mechanism.
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This work is part of the FUI project VoiceHome.
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Khouzaimi, H., Laroche, R., Lefèvre, F. (2017). Incremental Human-Machine Dialogue Simulation. In: Jokinen, K., Wilcock, G. (eds) Dialogues with Social Robots. Lecture Notes in Electrical Engineering, vol 427. Springer, Singapore. https://doi.org/10.1007/978-981-10-2585-3_4
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