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Special Issues
Recent Advances in Multimodal Conversational Systems
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Recent Advances in Multimodal Conversational Systems

A special issue of Multimodal Technologies and Interaction (ISSN 2414-4088).

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 3943

Special Issue Editors

Dr. David Griol

E-Mail Website
Guest Editor
Department of Computer Science, Universidad Carlos III de Madrid, Madrid, Spain
Dr. Zoraida Callejas Carrión

E-Mail Website
Guest Editor
SISDIAL Research Group, Universidad de Granada, 18071 Granada, Spain
Interests: dialogue systems; conversational systems; dialogue management; speech and language technologies; affective computing; emotion recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Conversational interfaces make it possible for humans to communicate with machines in natural language and have become widespread in the interaction with virtual assistants, mobile devices and smart environments. In multimodal and multisensory settings, different modalities can complement and enrich each other to provide additional flexibility and accuracy.

The aim of this Special Issue is to explore the multiple challenges of multimodal interaction going from sensing and signal processing, to understanding, learning and generating multimodal behaviour with implications in multiple and heterogeneous application domains.

Original papers are solicited on, but not limited to, the following areas:

  • multimodal conversational interfaces
  • dialogue systems, embodied conversational agents and their applications
  • theory and foundations of multimodal interaction
  • understanding multimodal human–human conversation
  • multimodal data capturing, annotation and representation
  • multimodal sensing and signal processing
  • modality representation, alignment and fusion
  • cross-modal learning
  • dialogue modelling and management
  • error detection and correction in multimodal interaction
  • user adaptation and personalization
  • expressive multimodal behaviour recognition and production
  • architectures to develop multimodal conversational interfaces
  • multimodal machine learning and data-driven approaches
  • end-to-end multimodal systems
  • data repositories, corpora, tools and resources
  • multimodal processing of social and emotional information
  • evaluation of multimodal interfaces
  • applications and relevant domains, e.g. virtual and smart environments, education, healthcare, conversational coaches, military.

Dr. David Griol
Dr. Zoraida Callejas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Multimodal Technologies and Interaction is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

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Research

12 pages, 1162 KiB  
Article
Information Processing and Overload in Group Conversation: A Graph-Based Prediction Model
by Gabriel Murray
Multimodal Technol. Interact. 2019, 3(3), 46; https://doi.org/10.3390/mti3030046 - 28 Jun 2019
Cited by 3 | Viewed by 3516
Abstract
Based on analyzing verbal and nonverbal features of small group conversations in a task-based scenario, this work focuses on automatic detection of group member perceptions about how well they are making use of available information, and whether they are experiencing information overload. Both [...] Read more.
Based on analyzing verbal and nonverbal features of small group conversations in a task-based scenario, this work focuses on automatic detection of group member perceptions about how well they are making use of available information, and whether they are experiencing information overload. Both the verbal and nonverbal features are derived from graph-based social network representations of the group interaction. For the task of predicting the information use ratings, a predictive model using random forests with verbal and nonverbal features significantly outperforms baselines in which the mean or median values of the training data are predicted, as well as significantly outperforming a linear regression baseline. For the task of predicting information overload ratings, the multimodal random forests model again outperforms all other models, including significant improvement over linear regression and gradient boosting models. However, on that task the best model is not significantly better than the mean and median baselines. For both tasks, we analyze performance using the full multimodal feature set versus using only linguistic features or only turn-taking features. While utilizing the full feature set yields the best performance in terms of mean squared error (MSE), there are no statistically significant differences, and using only linguistic features gives comparable performance. We provide a detailed analysis of the individual features that are most useful for each task. Beyond the immediate prediction tasks, our more general goal is to represent conversational interaction in such a way that yields a small number of features capturing the group interaction in an easily interpretable manner. The proposed approach is relevant to many other group prediction tasks as well, and is distinct from both classical natural language processing (NLP) as well as more current deep learning/artificial neural network approaches. Full article
(This article belongs to the Special Issue Recent Advances in Multimodal Conversational Systems)
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