Preface (to: Analysis of Verbal and Nonverbal Communication and Enactment: The Procesing Issues)
Preface (to: Analysis of Verbal and Nonverbal Communication and Enactment: The Procesing Issues)
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序言（至：言语和非言语交流与制定的分析：处理问题）
DOI：
10.1007/978-3-642-25775-9
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
A. Nijholt
中科院分区：
文献类型：
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作者：
A. Esposito;A. Vinciarelli;K. Vicsi;C. Pelachaud;A. Nijholt
本卷汇集了欧洲 COST 行动 2102“言语和非言语交流的跨模态分析”获得的先进研究成果，该成果主要在 2010 年 9 月 7 日至 10 日在匈牙利布达佩斯举行的 PINK SSPnet-COST 2102 国际会议“言语和非言语交流与制定分析：处理问题”上讨论（http://berber.tmit.bme.hu/cost2102/）。
该会议由信息和通信技术 (ICT) 领域的 COST（欧洲科学技术合作组织，www.cost.eu）和欧洲社会信号处理卓越网络 (SSPnet) 联合主办，旨在传播 COST 行动 2102 中开发的研究活动的进展：“言语和非言语交流的跨模态分析”(cost2102.cs.stir.ac.uk) （http://sspnet.eu/）。
会议的主要焦点是通过在环境和上下文中发出的言语和非言语信号来组合和积累知识的方法。在之前的会议中，COST 2102 重点讨论了发现和利用多模式信号传达的丰富信息的重要性。接下来的步骤是分析响应多模式信号而执行的行动，并研究如何在现实和社会可信的背景下组织这些行动。重点是处理问题，因为新方法计算复杂，并且要处理的数据量可能被认为在算法上不可行。因此，获取主动知识的数据处理必须考虑自然和直观的方法，更多地基于启发式和经验而不是符号，以及发现新的处理可能性，这些可能性考虑数据分析的新方法、通过同步和时间组织协调数据流以及提取特征的优化。
该卷的主题涵盖身体交流中的言语和非言语信息，语音、手势、目光和面部表情的跨模态分析，社会文化差异和个人特质，用于自动识别情绪、面部、面部表情和手势的多模态算法和程序，用于实现智能化身和交互式对话系统的音频和视频功能，虚拟通信代理和交互式对话系统。
This volume brings together the advanced research results obtained by the European COST Action 2102 “Cross Modal Analysis of Verbal and Nonverbal Communication,” primarily discussed at the PINK SSPnet-COST 2102 International Conference on “Analysis of Verbal and Nonverbal Communication and Enactment: The Processing Issues” held in Budapest, Hungary, September 7–10, 2010 (http://berber.tmit.bme.hu/cost2102/).
The conference was jointly sponsored by COST (European Cooperation in Science and Technology, www.cost.eu ) in the domain of Information and Communication Technologies (ICT) for disseminating the advances of the research activities developed within the COST Action 2102: “Cross-Modal Analysis of Verbal and Nonverbal Communication” (cost2102.cs.stir.ac.uk) and by the European Network of Excellence on Social Signal Processing, SSPnet (http://sspnet.eu/).
The main focus of the conference was on methods to combine and build up knowledge through verbal and nonverbal signals enacted in an environment and in a context. In previous meetings, COST 2102 focused on the importance of uncovering and exploiting the wealth of information conveyed by multimodal signals. The next steps have been to analyze actions performed in response to multimodal signals and to study how these actions are organized in a realistic and socially believable context. The focus was on processing issues, since the new approach is computationally complex and the amount of data to be treated may be considered algorithmically infeasible. Therefore, data processing for gaining enactive knowledge must account for natural and intuitive approaches, based more on heuristics and experiences rather than on symbols, as well as on the discovery of new processing possibilities that account for new approaches for data analysis, coordination of the data flow through synchronization and temporal organization and optimization of the extracted features.
The themes of the volume cover topics on verbal and nonverbal information in body-to-body communication, cross-modal analysis of speech, gestures, gaze and facial expressions, socio-cultural differences and personal traits, multimodal algorithms and procedures for the automatic recognition of emotions, faces, facial expressions, and gestures, audio and video features for implementing intelligent avatars and interactive dialogue systems, virtual communicative agents and interactive dialogue systems.