Transduction and Meaning–Making Issues Within Multimodal Messages

This paper analyzes transduction as an action of transposing information from one mode to another within the communication process and its implications in terms of meaning and coherence of a multimodal message. First, I discuss the multimodal method and its conjunction with some key concepts such as: sign, meaning, mode, transduction. Secondly, I approach transduction as an essential method of translating messages across the media variety, describing my interdisciplinary approach – that brings together semiotics and communications – and proposing a framework of explanation for transduction in the field of advertising. Drawing from a previous model (Culache 2015), I illustrate the way transduction takes place and identify its meaning-making issues while introducing the concept of ‘dominant mode.

Gesture and speech integration: an exploratory study of a man with aphasia

Background: In order to fully comprehend a speaker’s intention in everyday communication, we integrate information from multiple sources including gesture and speech. There are no published studies that have explored the impact of aphasia on iconic co-speech gesture and speech integration. Aims: To explore the impact of aphasia on co-speech gesture and speech integration in one participant with aphasia (SR) and 20 age-matched control participants. Methods & Procedures: SR and 20 control participants watched video vignettes of people producing 21 verb phrases in 3 different conditions, verbal only (V), gesture only (G) and verbal gesture combined (VG). Participants were required to select a corresponding picture from one of four alternatives: integration target, a verbal only match, a gesture only match, and an unrelated foil. The probability of choosing the integration target in the VG that goes beyond what is expected from the probabilities of choosing the integration target in V and G was referred to as multi-modal gain(MMG). Outcomes & Results: SR obtained a significantly lower multi-modal gain score than the control participants (p<0.05). Error analysis indicated that in speech and gesture integration tasks, SR relied on gesture in order to decode the message, whereas the control participants relied on speech in order to decode the message. Further analysis of the speech only and gesture only tasks indicated SR had intact gesture comprehension but impaired spoken word comprehension. Conclusions & Implications: The results confirm findings by Records (1994) which reported that impaired verbal comprehension leads to a greater reliance on gesture to decode messages. Moreover, multi-modal integration of information from speech and iconic gesture can be impaired in aphasia. The findings highlight the need for further exploration of the impact of aphasia on gesture and speech integration

ANGELICA : choice of output modality in an embodied agent

The ANGELICA project addresses the problem of modality choice in information presentation by embodied, humanlike agents. The output modalities available to such agents include both language and various nonverbal signals such as pointing and gesturing. For each piece of information to be presented by the agent it must be decided whether it should be expressed using language, a nonverbal signal, or both. In the ANGELICA project a model of the different factors influencing this choice will be developed and integrated in a natural language generation system. The application domain is the presentation of route descriptions by an embodied agent in a 3D environment. Evaluation and testing form an integral part of the project. In particular, we will investigate the effect of different modality choices on the effectiveness and naturalness of the generated presentations and on the user's perception of the agent's personality

Vision systems with the human in the loop

The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed

Elckerlyc in practice - on the integration of a BML Realizer in real applications

Building a complete virtual human application from scratch is a daunting task, and it makes sense to rely on existing platforms for behavior generation. When building such an interactive application, one needs to be able to adapt and extend the capabilities of the virtual human offered by the platform, without having to make invasive modications to the platform itself. This paper describes how Elckerlyc, a novel platform for controlling a virtual human, offers these possibilities

A system design for human factors studies of speech-enabled Web browsing

This paper describes the design of a system which will subsequently be used as the basis of a range of empirical studies aimed at discovering how best to harness speech recognition capabilities in multimodal multimedia computing. Initial work focuses on speech-enabled browsing of the World Wide Web, which was never designed for such use. System design is complete, and is being evaluated via usability testing

Affective Medicine: a review of Affective Computing efforts in Medical Informatics

Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field

The DRIVE-SAFE project: signal processing and advanced information technologies for improving driving prudence and accidents

In this paper, we will talk about the Drivesafe project whose aim is creating conditions for prudent driving on highways and roadways with the purposes of reducing accidents caused by driver behavior. To achieve these primary goals, critical data is being collected from multimodal sensors (such as cameras, microphones, and other sensors) to build a unique databank on driver behavior. We are developing system and technologies for analyzing the data and automatically determining potentially dangerous situations (such as driver fatigue, distraction, etc.). Based on the findings from these studies, we will propose systems for warning the drivers and taking other precautionary measures to avoid accidents once a dangerous situation is detected. In order to address these issues a national consortium has been formed including Automotive Research Center (OTAM), Koç University, Istanbul Technical University, Sabancı University, Ford A.S., Renault A.S., and Fiat A. Ş