中野 有紀子

ジェスチャーや表情など、さまざまな非言語情報は2者間対話の世界において重要な役割を担っている。 本研究の第一の目的は，多言語話者による二者間相互作用における非言語情報の差異を分析することである．そこで，文化的背景の異なる3カ国で収集されたマルチモーダルな会話データセットであるNoXiデータコーパスを用いて，ANOVA分析により3カ国の対話ペアグループ間で異なる非言語情報を分析し、報告する。

本論文では，マルチモーダル特徴量を用いた議論の質の推定モデルの精度を改善するための手法を提案する.計56回のグループミーティングで観測された参加者の韻律・表情・言語・発話ターンの特徴量を含むグループ会議コーパスMATRICSを用いる．先行研究で課題となっていた時系列データに含まれる全てのフレーム・全てのモダリティの特徴量が, そのラベルの推定に有効であるとは限らないという問題に対して，ノイズラベルに有効な弱教師あり学習であるCo-teachingをよりノイズに対して頑健に拡張したN-teachingモデルを提案する．またノイズとして学習に使われなかったサンプルについて分析を行い，先行研究との比較を行った．本研究では議論内容のOriginally（新規性）の指標においてMAE 0.309という最高精度を得た．

Case studies of group discussions are considered an effective way to assess communication skills (CS). This method can help researchers evaluate participants’ engagement with each other in a specific realistic context. In this article, multimodal analysis was performed to estimate CS indices using a three-task-type group discussion dataset, the MATRICS corpus. The current research investigated the effectiveness of engaging both static and time-series modeling, especially in task-independent settings. This investigation aimed to understand three main points: first, the effectiveness of time-series modeling compared to nonsequential modeling; second, multimodal analysis in a task-independent setting; and third, important differences to consider when dealing with task-dependent and task-independent settings, specifically in terms of modalities and prediction models. Several modalities were extracted (e.g., acoustics, speaking turns, linguistic-related movement, dialog tags, head motions, and face feature sets) for inferring the CS indices as a regression task. Three predictive models, including support vector regression (SVR), long short-term memory (LSTM), and an enhanced time-series model (an LSTM model with a combination of static and time-series features), were taken into account in this study. Our evaluation was conducted by using the R² score in a cross-validation scheme. The experimental results suggested that time-series modeling can improve the performance of multimodal analysis significantly in the task-dependent setting (with the best R² = 0.797 for the total CS index), with word2vec being the most prominent feature. Unfortunately, highly context-related features did not fit well with the task-independent setting. Thus, we propose an enhanced LSTM model for dealing with task-independent settings, and we successfully obtained better performance with the enhanced model than with the conventional SVR and LSTM models (the best R² = 0.602 for the total CS index). In other words, our study shows that a particular time-series modeling can outperform traditional nonsequential modeling for automatically estimating the CS indices of a participant in a group discussion with regard to task dependency.

In aging societies, supporting elderly people is a critical issue, and companion agents that can function as a conversational partner are expected to provide social support to isolated older adults. Aiming at improving companionship dialogues with these agents, this study proposes a topic selection mechanism using blog articles written by the elderly. By categorizing the nouns extracted from blogs using Wikipedia, we defined 219 topic categories consisting of about 3,000 topic words that the elderly discuss in their daily life. The topic selection mechanism is implemented into a companion agent and used to generate the agent's utterances.

A robot's gaze behaviors are indispensable in allowing the robot to participate in multiparty conversations. To build a robot that can convey appropriate attentional behavior in multiparty human-robot conversations, this study proposes robot head gaze models in terms of participation roles and dominance in a conversation. By implementing such models, we developed a robot that can determine appropriate gaze behaviors according to its conversational roles and dominance.

Group discussions are used widely when generating new ideas and forming decisions as a group. Therefore, it is assumed that giving social influence to other members through facilitating the discussion is an important part of discussion skill. This study focuses on influential statements that affect discussion flow and highly related to facilitation, and aims to establish a model that predicts influential statements in group discussions. First, we collected a multimodal corpus using different group discussion tasks in-basket and case-study. Based on schemes for analyzing arguments, each utterance was annotated as being influential or not. Then, we created classification models for predicting influential utterances using prosodic features as well as attention and head motion information from the speaker and other members of the group. In our model evaluation, we discovered that the assessment of each participant in terms of discussion facilitation skills by experienced observers correlated highly to the number of influential utterances by a given participant. This suggests that the proposed model can predict influential statements with considerable accuracy, and the prediction results can be a good predictor of facilitators in group discussions.

In conversational agents with multiparty communication functionality, a system needs to be able to identify the addressee for the current floor and respond to the user when the utterance is addressed to the agent. This study proposes some addressee identification models based on speech and gaze information, and tests whether the models can be applied to different proxemics. We build an addressee identification mechanism by implementing the models and incorporate it into a fully autonomous multiparty conversational agent. The system identifies the addressee from online multimodal data and uses this information in language understanding and dialogue management. Finally, an evaluation experiment shows that the proposed addressee identification mechanism works well in a real-time system, with an F-measure for addressee estimation of 0.8 for agent-addressed utterances. We also found that our system more successfully avoided disturbing the conversation by mistakenly taking a turn when the agent is not addressed.

With the increasing average life expectancy of world population, there are more and more dementia patients, and the needs of assistive technology emerges. According to the literature, the progress of cognitive impairment can be suppressed to be slower if the patients is constantly in calm mood. An effective way is suggested by keeping them in social relationship with others. With the goal of developing a conversational humanoid that can serve as a companion for dementia patients, we propose an autonomous virtual agent that can generate back channel feedback, such as head nods and verbal acknowledgment, on the basis of acoustic information in the user's speech. The system is also capable of speech recognition and language understanding functionalities. In order to compensate the companionship of the agent and the ability to assist the user's memory, we are developing a memory vest which is equipped with portable devices including an Android smartphone, two IC audio recorders, and a digital video recorder to log the daily life of the patient. The gathered activity history database can then be used to enrich the dialogue ability of the agent and for helping the user to recall his / her own memory. © 2012 IEEE.

This paper proposes a direction giving avatar system in Metaverse, which automatically generates direction giving gestures based on linguistic information obtained from the user's chat text input and spatial information in Metaverse. First, we conduct an experiment to collect direction giving conversation corpus. Then, using the collected corpus, we analyze the relationship between the proxemics of conversation participants and the position of their direction giving gestures. Next, we analyze the relationship between linguistic features in direction giver's utterances and the shape of their spatial gestures. We define five categories of gesture concepts and four gesture shape parameters, and analyze the relationship between the gesture concepts and a set of gesture parameters. Based on these results, we propose an automatic gesture decision mechanism and implement a direction giving avatar system in Metaverse.

It is important for conversational agents that manage multiparty conversations to recognize the group dynamics existing among the users. This paper proposes a method for estimating the conversational dominance of participants in group interactions. First, we conducted a Wizard-of-Oz experiment to collect conversational speech, and motion data. Then, we analyzed various paralinguistic speech and gaze behaviors to elucidate the factors that predict conversational dominance. Finally, by exploiting the speech and gaze data as estimation parameters, we created a regression model to estimate conversational dominance, and the multiple correlation coefficient of this model was 0.85.

本研究は，旅行代理店を題材として，二人の客の間でなされる意思決定会話を支援する情報提供エージェントを開発するものである．本システムは，ユーザの発話状態と顔向きの変化から，会話内容が相談，雑談，質問，理解のどの状態であるかを推定し，会話制御機構において，四種類の状態に応じてエージェントによる会話への参入方法を決定する．指標の有効性と推定精度を，被験者評価実験によって検証し，その結果を報告する．

This paper proposes a method for assigning gestures to text based on lexical and syntactic information. First, our empirical study identified lexical and syntactic information strongly correlated with gesture occurrence and suggested that syntactic structure is more useful for judging gesture occurrence than local syntactic cues. Based on the empirical results, we have implemented a system that converts text into an animated agent that gestures and speaks synchronously. 1

In this paper, we describe an embodied conversational kiosk that builds on research in embodied conversational agents (ECAs) and on information displays in mixed reality and kiosk format in order to display spatial intelligence. ECAs leverage people’s abilities to coordinate information displayed in multiple modalities, particularly information conveyed in speech and gesture. Mixed reality depends on users ’ interactions with everyday objects that are enhanced with computational overlays. We describe an implementation, MACK (Media lab Autonomous Conversational Kiosk), an ECA who can answer questions about and give directions to the MIT Media Lab’s various research groups, projects and people. MACK uses a combination of speech, gesture, and indications on a normal paper map that users place on a table between themselves and MACK. Research issues involve users’ differential attention to hand gestures, speech and the map, and flexible architectures for Embodied Conversational Agents that allow these modalities to be fused in input and generation.

Abstract. Embodied Conversational Agents (ECAs) are computer generated life-like characters that interact with human users in face-to-face conversations. To achieve natural multi-modal conversations, ECA systems are very sophisticated and require many building assemblies and thus are difficult for individual research groups to develop. This paper proposes a generic architecture, the Universal ECA Framework, which is currently under development and includes a blackboard-based platform, a high-level protocol to integrate general purpose ECA components and ease ECA system prototyping. 1. The Essential Components of Embodied Conversational Agents and the Issues to Integrate Them Embodied Conversational Agents (ECAs) are computer generated life-like characters that interact with human users in face-to-face conversations. To achieve natural communications with human users, many software or hardware assemblies are required in an ECA system. By their functionalities in the information flow of the interactions with human users, they can be divided into four categories:

This paper addresses the problem of designing embodied conversational agents that exhibit appropriate posture shifts during dialogues with human users. Previous research has noted the importance of hand gestures, eye gaze and head nods in conversations between embodied agents and humans. However, this research has neglected the role of other body movements, in particular postural shifts. We present an analysis of human monologues and dialogues that suggests that postural shifts can be predicted as a function of discourse state in monologues, and discourse state and conversation state in dialogues. On the basis of these findings, we have implemented an embodied conversational agent that uses a dialogue manager called Collagen in such a way as to generate postural shifts.

We investigate the verbal and nonverbal means for grounding, and propose a design for embodied conversational agents that relies on both kinds of signals to establish common ground in human-computer interaction. We analyzed eye gaze, head nods and attentional focus in the context of a direction-giving task. The distribution of nonverbal behaviors differed depending on the type of dialogue move being grounded, and the overall pattern reflected a monitoring of lack of negative feedback. Based on these results, we present an ECA that uses verbal and nonverbal grounding acts to update dialogue state.

While recent advancements in virtual reality technology have created a rich communication interface linking humans and computers, there has been little work on building dialogue systems for 3D virtual worlds. This paper proposes a method for altering the instruction dialogue to match the user's view in a virtual environment. We illustrate the method with the system MID-3D, which interactively instructs the user on dismantling some parts of a car. First, in order to change the content of the instruction dialogue to match the user's view, we extend the refinement-driven planning algorithm by using the user's view as a plan constraint. Second, to manage the dialogue smoothly, the system keeps track of the user's viewpoint as part of the dialogue state and uses this information for coping with interruptive subdialogues. These mechanisms enable MID-3D to set instruction dialogues in an incremental way; it takes account of the user's view even when it changcs frequently.

The purpose of this pape is to identify effective factors for selecting discourse organization cue phrases in instruction dialogue that signM changes in discourse structure such as topic shifts and attentional state changes. By using a machine learning technique, a variety of features concerning discourse structure, task structure, and dialogue context are examined in terms of their effectiveness and the best set of learning 2eaturea is identified. Our result reveals that, in addition to discourse structure, already identified in previous studies, task structure and alogue context play an important role. Moreover, an evaluation using a large dialogue corpus shows the utility of applying machine learning techniques to cue phrase selection.

The aim of this paper is to develop animated agents that can control multimodal instruction dialogues by monitoring user’s behaviors. First, this paper reports on our Wizard-of-Oz experiments, and then, using the collected corpus, proposes a probabilistic model of fine-grained timing dependencies among multimodal communication behaviors: speech, gestures, and mouse manipulations. A preliminary evaluation revealed that our model can predict a instructor’s grounding judgment and a listener’s successful mouse manipulation quite accurately, suggesting that the model is useful in estimating the user’s understanding, and can be applied to determining the agent’s next action. 1

This paper addresses the issue of designing embodied conversational agents that exhibit appropriate posture shifts during dialogues with human users. Previous research has noted the importance of hand gestures, eye gaze and head nods in conversations between embodied agents and humans. We present an analysis of human monologues and dialogues that suggests that postural shifts can be predicted as a function of discourse state in monologues, and discourse and conversation state in dialogues. On the basis of these findings, we have implemented an embodied conversational agent that uses Collagen in such a way as to generate postural shifts. 1.

Abstract. We present our concept of integrating culture as a computational parameter for modeling multimodal interactions with virtual agents. As culture is a social rather than a psychological notion, its influence is evident in interactions, where cultural patterns of behavior and interpretations mismatch. Nevertheless, taking culture seriously its influence penetrates most layers of agent behavior planning and generation. In this article we concentrate on a first meeting scenario, present our model of an interactive agent system and identify, where cultural parameters play a role. To assess the viability of our approach, we outline an evaluation study that is set up at the moment. 1

Abstract. The goal of this paper is to integrate culture as a computational term in embodied conversational agents by employing an empirical data-driven approach as well as a theoretical model-driven approach. We propose a parameter-based model that predicts nonverbal expressions appropriate for specific cultures. First, we introduce the Hofstede theory to describe socio-cultural characteristics of each country. Then, based on the previous studies in cultural differences of nonverbal behaviors, we propose expressive parameters to characterize nonverbal behaviors. Finally, by integrating socio-cultural characteristics and nonverbal expressive characteristics, we establish a Bayesian network model that predicts posture expressiveness from a country name, and vice versa.

Abstract. Trying to adapt the behavior of an interactive system to the cultural background of the user requires information on how relevant behaviors differ as a function of the user’s cultural background. To gain such insights in the interrelation of culture and behavior patterns, the information from the literature is often too anecdotal to serve as the basis for modeling a system’s behavior, making it necessary to collect multimodal corpora in a standardized fashion in different cultures. In this chapter, the challenges of such an endeavor are introduced and solutions are presented by examples from a German-Japanese project that aims at modeling culture-specific behaviors for Embodied Conversational Agents. 1