## 2 Related Work  
The term \"embodied conversational agent\" was coined by [PERSON] [1; 2]. In [2], he listed the following properties an ECA should have:  
* Recognizing and responding to verbal and nonverbal input;
* Generating verbal and nonverbal output;
* Dealing with conversational functions, such as turn-taking, feedback, and repair mechanisms;
* Giving signals that indicate the state of the conversation, as well as contributing new propositions to the discourse.  
Nonverbal behaviors such as nodding or changing facial expressions are the special features of an ECA compared with conversational agents without bodies. [PERSON] et al. developed an ECA named Rea [22], which behaved as a real estate agent. Rea had most of the basic functionalities an ECA should have; however, most functions are realized using handwritten rules. Since then, many ECAs have been developed [23; 24].  
The emotional behavior of an ECA, including perception of the user's emotion and expressing emotion using facial expression, is an important aspect when designing an ECA. The effect of introducing emotion in an ECA was first discussed in the late 1990s [25].  
The main objective of introducing emotional behavior into ECAs is to enhance the user's impression of the ECAs. Expressing positive emotion is especially expected to improve this impression [26]. Since then, many studies have been conducted to develop general models of emotional dynamics [27; 28; 29]. Not only are facial expressions and gestures effective but so is the paralinguistic property of speech in presenting specific emotions of ECAs [30; 31]. Conventional models use rules to determine the ECAs' emotional behavior, while recent studies use more sophisticated models based on deep learning. For example, [PERSON] et al. proposed a model based on the multimodal-attention-based conditional variational autoencoder (M-CVAE) [32].  
Since the aim of introducing emotional behavior into ECAs is to improve the user's impression of the system, such behaviors are often used in ECAs for clinical or healthcare purposes [33; 34; 35; 36].  
As listed above, most studies have focused on how emotional behavior makes ECAs more human-like and improves the user's impression. On the other hand, emotional behavior could interact with other factors of dialogue, such as response delay. This is the duration from the end of the user's utterance to the beginning of the ECA's utterance. As mentioned before, the response delay affects the user's cognition performance [14]. [PERSON] et al. reported that 0 to 250 ms of delay gave the best satisfaction and usability for in-vehicle conversational agents [37]. [PERSON] et al. reported an interesting result on the user's preference for the response delay [38]; they proved that taking a long time to answer complicated questions improved the user's impression of the system and perception of humanness. This result is considered a realization of response delay distribution according to different question types [15]. [PERSON] et al. showed that the response delay varies according to the dialogue act of the previous utterance [39].  
As described, there are studies on how linguistic content affects the response delay; however, no research has been conducted on how emotional behavior and the response delay jointly affect the ECA's impression. If we find any relationship between emotional behavior and response delay, it can be used to make the dialogue behavior of ECAs more natural.