# Evaluation of Emotional Satisfaction Using Questionnaires in Voice-Based Human-AI Interaction
## 1 Introduction  
Recently, artificial intelligence based on various technologies, such as machine learning, natural language processing, machine vision, and big data, has been applied to systems in various fields to be used as user agents or mutual cooperation models [1]. A representative intelligent system is a voice-based intelligent system (VIS) in the form of a chatbot, which is created from developed speech recognition technology, such as natural language processing and text-to-speech. Fierce competition is underway to preempt the market for agent technology using VIS in artificial intelligence speakers and smartphones [2]. Currently, VIS is mainly used to meet customer information requirements through the role of a chatbot helper or virtual assistant, which is used to interact with customers. Interaction with AI-infused systems is defined as human-AI interaction (HAII) [3] and conceived as a modified version of human-computer interaction (HCI), which provides differentiated interaction [4]. Humans have performed system-driven interaction in HCI, whereas intelligent systems are required to provide user-centered interaction that adapts to a specified context of use in HAII. [PERSON] et al. [5] investigated the impact of satisfaction and personalization using user reviews of VIS integrated into real life, and [PERSON] et al. [6] reported that there is a need for personalization and customization in interaction with intelligent systems. Therefore, there is a demand to create an interaction design that can secure the emotional satisfaction of various users as an intelligent system is developed. According to [PERSON]'s \"hierarchy of human needs\", the final expectation of a user from the system is defined as satisfaction (Figure 1) [7]. This means that the user wants to be satisfied emotionally while interacting with the system, beyond usability. Satisfaction is a feeling that arises from the mind during the process of perceiving and recognizing the information presented by the system, indicating a state or feeling in which a need is fulfilled. Therefore, it can be stated that a person is satisfied when that person is fulfilled emotionally during interaction with the system. To quantitatively identify personal satisfaction, human emotions should first be addressed accurately. Brain-computer interface (BCI) technology is a way to quantify human emotions that can be used to infer satisfaction [8]. The BCI uses one of the biosignals, brain activity, to decode human thoughts, such as emotion [9; 10], attention [11; 12; 13; 14; 15], and vigilance [16]. The decoded outputs can be used to identify the type of emotion (e.g., happy, angry, and excited), level of attention, or vigilance directly. Eye movement can also be used to explore the current status (emotion) of a human during a specific task [17]. To utilize these methods to decode human emotion in detail, however, the characteristics of biosignals in each emotional state need to be defined precisely in advance. It is difficult to secure sufficient sensitivity for classification without these precise criteria. On the contrary, there is another way to quantitatively identify emotional satisfaction, called Kansei engineering or affective engineering [18]. Through this, it is possible to evaluate the user's emotional satisfaction with the product, or to identify the design parameters of the system to enhance the emotional satisfaction of the user. [PERSON] et al. [19] presented a method for optimizing user emotion using the Kansei engineering approach for product design, and [PERSON] et al. [20] proposed a framework for evaluating users' emotional satisfaction using Kansei engineering. One advantage of the Kansei engineering approach is its relatively higher resolution in classifying users' emotional states, because they are evaluated in terms of a questionnaire consisting of a variety of adjectives that express feelings and emotions [21].