Human-Machine Interaction through Multi-Modal Interface with Combination of Speech, Text, Image and Sensor Data


In the context of human–computer interaction, a modality is the classification of a single independent channel of sensory input / output between a computer and a human. A system is designated uni-modal if it has only one modality implemented, and multi-modal if it has more than one. When multiple modalities are available for some tasks or aspects of a task, the system is said to have overlapping modalities. If multiple modalities are available for a task, the system is said to have redundant modalities. Multiple modalities can be used in combination to provide complementary methods that may be redundant but convey information more effectively. Modalities can be generally defined in two forms: human-computer and computer-human modalities.

With the increasing popularity of smartphones, the general public are becoming more comfortable with the more complex modalities. Speech recognition was a major selling point of the iPhone and following Apple products, with the introduction of Siri. This technology gives users an alternative way to communicate with computers when typing is less desirable. However, in a loud environment, the audition modality is not quite effective. This exemplifies how certain modalities have varying strengths depending on the situation. Other complex modalities such as computer vision in the form of Microsoft's Kinect or other similar technologies can make sophisticated tasks easier to communicate to a computer especially in the form of three dimensional movement.

This talk is based on a physical robot (a personalized humanoid built in MUST Research), equipped with various types of input devices and sensors to allow them to receive information from humans, which are interchangeable and a standardized method of communication with the computer, affording practical adjustments to the user, providing a richer interaction depending on the context, and implementing robust system with features like; keyboard; pointing device; touchscreen; computer vision; speech recognition; motion, orientation etc.

Cognitive computing makes a new class of problems computable.   To respond to the fluid nature of users understanding of their problems, the cognitive computing system offers a synthesis not just of information sources but of influences, contexts, and insights.   These systems differ from current computing applications in that they move beyond tabulating and calculating based on pre-configured rules and programs.   They can infer and even reason based on broad objectives. In this sense, cognitive computing is a new type of computing with the goal of more accurate models of how the human brain or mind senses, reasons, and responds to stimulus.   It is a field of study which studies how to create computers and computer software that are capable of intelligent behavior. This field is interdisciplinary, in which a number of sciences and professions converge, including computer science, electronics, mathematics, statistics, psychology, linguistics, philosophy, neuroscience and biology.

Computer–Human Modalities

Computers utilize a wide range of technologies to communicate and send information to humans:

  • Vision – computer graphics typically through a screen
  • Audition – various audio outputs
  • Tactition – vibrations or other movement
  • Gustation (taste)
  • Olfaction (smell)
  • Thermoception (heat)
  • Nociception (pain)
  • Equilibrioception (balance)

Human–computer Modalities

Computers can be equipped with various types of input devices and sensors to allow them to receive information from humans. Common input devices are often interchangeable if they have a standardized method of communication with the computer and afford practical adjustments to the user. Certain modalities can provide a richer interaction depending on the context, and having options for implementation allows for more robust systems.

  • Keyboard
  • Pointing device
  • Touchscreen
  • Computer vision
  • Speech recognition
  • Motion
  • Orientation

Project Features

Adaptive: They MUST learn as information changes, and as goals and requirements evolve. They MUST resolve ambiguity and tolerate unpredictability. They MUST be engineered to feed on dynamic data in real time.

Interactive: They MUST interact easily with users so that those users can define their needs comfortably. They MUST interact with other processors, devices, services, as well as with people.

Iterative and Stateful: They MUST aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They MUST remember previous interactions in a process and return information that is suitable for the specific application at that point in time.

Contextual: They MUST understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulation, user profile, process, task and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided).

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Outline/Structure of the Talk



Applications (MUSTie - Human-Machine Interaction through Multi-Modal Interface)

Interaction via:

  • Speech
  • Text
  • Image
  • Sensor Data


Market and Social Impact

Social Impact

Sustainability and Inter-operability

Collaboration and Ecosystem


Learning Outcome

Using Multiple Modalities

Having multiple modalities in a system gives more affordance to users and can contribute to a more robust system. Having more also allows for greater accessibility for users who work more effectively with certain modalities. Multiple modalities can be used as backup when certain forms of communication are not possible. This is especially true in the case of redundant modalities in which two or more modalities are used to communicate the same information. Certain combinations of modalities can add to the expression of a computer-human or human-computer interaction because the modalities each may be more effective at expressing one form or aspect of information than others.
There are six types of cooperation between modalities, and they help define how a combination or fusion of modalities work together to convey information more effectively.

• Equivalence: information is presented in multiple ways and can be interpreted as the same information
• Specialization: when a specific kind of information is always processed through the same modality
• Redundancy: multiple modalities process the same information
• Complimentarity: multiple modalities take separate information and merge it
• Transfer: a modality produces information that another modality consumes
• Concurrency: multiple modalities take in separate information that is not merged

Target Audience

Anyone who has interest to build AI systems across the planet.

Prerequisites for Attendees

Basic Mathematics, Statistics and Programming!

schedule Submitted 4 months ago

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    key measure

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    • completeness
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    “Alexa, launch Netflix!”

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    Network optimisation

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