Four Easy Pieces for Assessing the Usability of Multimodal Interaction: The Care Properties

Coutaz, Joëlle; Nigay, Laurence; Salber, Daniel; Blandford, Ann; May, Jon; Young, Richard M.

doi:10.1007/978-1-5041-2896-4_19

Cited by 187 publications

(153 citation statements)

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“…Finally, spoken disfluencies can also be reduced by designing multimodal interfaces that include another adapted modality, which can be used complementarily or redundantly with speech [36].…”

Section: Error Reduction By Designmentioning

confidence: 99%

“…This has been called "agent assignment" in the CARE (Complementarity, Assignment, Redundancy, and Equivalence) usability framework [36]. CARE describes two types of assignment: strict assignment and agent assignment.…”

Section: User Preventionmentioning

confidence: 99%

“…Complementarity describes the conjoint use of two modalities, whereby each modality conveys only partial information, but the integration of the two results in a complete and comprehensible message [36]. To be complementary, both modalities must be semantically rich, i.e.…”

Section: User Preventionmentioning

confidence: 99%

“…Redundancy also describes the conjoint use of two modalities, but in this case the two modalities convey the same information [36]. For example, users produce redundant inputs when they choose to both speak and write the words of a message.…”

Section: User Preventionmentioning

confidence: 99%

“…As for complementary inputs, ambiguities in one modality can be resolved based on the second representation of the same information in the other modality. Redundancy implies that the two modalities are equivalent [36], which means that they are able to convey similar semantic content and can be used interchangeably. According to [45], equivalence fails to acknowledge the fact that different modalities are likely to differ in the type of information they transmit, their functionality during communication, and the way they are integrated with other modalities.…”

Section: User Preventionmentioning

confidence: 99%

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Towards a taxonomy of error-handling strategies in recognition-based multi-modal human–computer interfaces

Bourguet

2006

Signal Processing

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In this paper, we survey the different types of error-handling strategies that have been described in the literature on recognition-based human-computer interfaces. A wide range of strategies can be found in spoken human-machine dialogues, handwriting systems, and multimodal natural interfaces.We then propose a taxonomy for classifying error-handling strategies that has the following three dimensions: the main actor in the error-handling process (machine versus user), the purpose of the strategy (error prevention, discovery, or correction), and the use of different modalities of interaction. The requirements that different error-handling strategies have on different sets of interaction modalities are also discussed. The main aim of this work is to establish a classification that can serve as a tool for understanding how to develop more efficient and more robust multimodal human-machine interfaces.Keywords: recognition-based technology, multimodal interfaces, error-handling, taxonomy, interaction design, interaction robustness Recognition-based technologyMultimodal interaction refers to interaction with the virtual and physical environment through natural modes of communication such as speech, body gestures, handwriting, graphics, or gaze.Unlike keyboards and mice inputs, natural modes of communication usually are non-deterministic, and have to be "recognised" by a recognition system, before they can be passed on to an application.Recent developments in recognition-based technology (e.g. speech and gesture recognition) have opened a myriad of new possibilities for the design and implementation of multimodal applications.Handwriting recognisers, for example, are being used in personal digital assistants (e.g. Paragon's multilingual PenReader software for Pocket PC devices), and speech recognition has made its way into desktop machines (e.g. IBM's ViaVoice TM speech recognition engines). However, designing and

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Section: Error Reduction By Designmentioning

confidence: 99%

Section: User Preventionmentioning

confidence: 99%

Section: User Preventionmentioning

confidence: 99%

Section: User Preventionmentioning

confidence: 99%

Section: User Preventionmentioning

confidence: 99%

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Towards a taxonomy of error-handling strategies in recognition-based multi-modal human–computer interfaces

Bourguet

2006

Signal Processing

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Software Architecture Modeling for User Interfaces

Coutaz

2002

Encyclopedia of Software Engineering

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Architectural modeling is becoming a central problem for large, complex systems. With the advent of new technologies and user-centered concerns, the user interface portion of interactive systems is becoming increasingly large and complex. This chapter is a reflection on software architecture modeling for interactive systems. In this domain, a number of architectural frameworks have emerged as canonical references. Although these models provide useful insights about how to partition and organize interactive systems, they do not always address important problems identified by main stream software architecture modeling. We first introduce the notion of software architecture and make explicit the steps and issues that architectural design involves and that most software designers tend to blend in a rather fuzzy way. Building on general concepts, a comparative analysis of the most significant refernce models developed for interactive systems is then presented: the Seeheim and Arch models followed by the more recent agent-based approaches that can be shaped to support multimodal interaction and groupware.

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Multimodal Output Management

Landragin¹

2013

Man–Machine Dialogue

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Intelligent multimedia presentation systems (IMMPS) have to take into account pragmatics and human factors such as the specificities of human perception, attention, memory, conceptualization, and language. Using a conversational animated agent or not, some principles can be followed to increase the communicative abilities of interactive systems. In this paper we propose a set of such principles. We exploit our background in natural language processing and computational pragmatics to provide specifications for multimodal systems. The classifications and principles and architectural concerns we present are based on some experimental observations (that are not described here) and constitute a kind of white paper for future implementations.

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Four Easy Pieces for Assessing the Usability of Multimodal Interaction: The Care Properties

Cited by 187 publications

References 1 publication

Towards a taxonomy of error-handling strategies in recognition-based multi-modal human–computer interfaces

Towards a taxonomy of error-handling strategies in recognition-based multi-modal human–computer interfaces

Software Architecture Modeling for User Interfaces

Multimodal Output Management

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