Person Identification via Multi-Modal Interface with Combination of Speech and Image Data
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. For example, MUST researchers are working on a personalized humanoid built and 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.
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
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
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).
Multi-Modal Interaction: https://www.youtube.com/watch?v=jQ8Gq2HWxiA
Gesture Detection: https://www.youtube.com/watch?v=rDSuCnC8Ei0
Speech Recognition: https://www.youtube.com/watch?v=AewM3TsjoBk
Assignment (Hands-on Challenge for Attendees)
Real-time multi-modal access control system for authorized access to work environment - All the key concepts and individual steps will be demonstrated and explained in this workshop, and the attendees need to customize the generic code or approach for this assignment or hands-on challenge.
Outline/Structure of the Workshop
Person Identification and verification only based on one entity (either face recognition or voice recognition) have many limitations. For example, in insufficient light conditions or if there is any problem with the camera lens, person identification or verification will fail. Similarly, when background noise is too much, speaker identification will also be difficult. So in such cases, multi-modal system is required and is more robust and will provide a better performance.
This workshop will target person identification and verification using multi-modal analysis like using both computer vision and audio processing
- Introduction to Multi-Modal Learning (MML) using Deep Learning (10 mins)
- Demonstrating model performance for Person Identification using multi-modality and comparing the performance with that of individual modalities. (10 mins)
- Hands-on assignment to carry out person identification using late fusion multi-modal technique using image and speech data (70 mins)
Requirement for attendees:
Access to Google Colab using their Google Accounts
1. Open Google Colab: https://colab.research.google.com/
2. Go to File -> Open Notebook
3. Select Github option
4. Paste the github notebook link: https://github.com/adib0073/ODSC_2019-Multi-Modal-Learning/blob/master/odsc_workshop_main.ipynb
5. Once the notebook is opened, connected to the run-time environment after signing in with your Google account
6. While running the notebook, uncomment the specific Google Colab environment lines of code
7. Change the required file path/directory as and when required to match the mounted Google Colab path.
- Introductory concepts about Multi-Modal Learning
- Performance analysis for individual modality performance for face and speech recognition models
- Combining modalities using Late fusion technique for Person Identification from image and audio data and analyzing the combined performance.
Anyone who has interest to build AI systems across the planet.
Prerequisites for Attendees
- Basic Mathematics, Statistics and Programming with Python!
- Basic knowledge on Neural-Nets
- Basic knowledge on computer vision and audio-processing
Pre-requisite for preparing the assignment dataset preparation:
- Image Data: JPG Image of size 250px X 250px : Image can be edited in any paint tool like paint brush
- Audio Data: Key Phrase while recording: 'i am going to make him an offer he cannot refuse', Audio sampling rate: '16000 Hz', 'Mono- channel', '16 bit PCM' - suggested tool: Audacity
schedule Submitted 7 months ago
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“Alexa, launch Netflix!”
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