Utterance selection for speech acts in a cognitive tourguide scenario
Felix Putze, Tanja Schultz · 2010
Abstract This paper describes the integration of a cognitive memorymodel into a spoken dialog system for an in-car tourguide ap-plication. This memory model enhances the capabilities of thesystem and of the simulated user by estimating if and whichinformation is relevant and useful in a given situation. An eval-uation study with 15 human judges is performed to demonstratethe feasibility of the described approach. The results show thatthe proposed utterance selection strategy and the memory modelsignificantly improve the human-like interaction behavior of thespoken dialog system in terms of the amount and quality ofgiven information, relevance, manner, and naturalness of thespoken interaction.Index Terms: spoken interaction, cognition, memory model,workload, utterance selection 1. Introduction Spoken dialog systems (SDS) have matured to a point wherethey find their way into many real-world applications. How-ever, their application in very dynamic scenarios remains anopen and very challenging task. In our application, we imple-ment a virtual co-driver in the car that acts as a tourguide duringa ride. While a traditional SDS already offers an eyes-free andhands-free control for in-car information applications, the par-allel driving task uses the user’s cognitive capacity so we canno longer assume to deal with a fully attentive and perfect inter-action partner as in more static environments. Additionally, wehave to deal with an ever-changing context in the dynamic en-vironment. Therefore, we need to integrate components in ourdialog systems that are able to explicitly model, predict, andcope with this imperfect user and the varying focus to ensure aseamless and successful dialog experience.Especially in interaction scenarios which are not directlytask-driven like our tourguide scenario, utterance selection isnot trivial as while we still follow a clearly defined goal of pro-viding as much interesting information as possible, but have noclear order or priority of information chunks to present. Thesame is true if we want to simulate a user for evaluation or au-tomatic strategy learning. To create coherent user behavior, weneed to establish what the simulated users currently have ontheir mind. An explicit memory model aims for a detailed rep-resentation of human memory by dynamically modeling an in-dividual strength of activation for each chunk of information.This paper describes the implementation of the model and howit is applied to utterance selection for both the system and thesimulated user. The primary goal of the utterance selection is tofind an utterance that is most relevant in the current context andof most interest to the user.