1.
Title Slide
Title: Innovative Video Conferencing App for Inclusive Communication
Subtitle: Bridging the Communication Gap
Your Name, Team Name, or Organization
2. Real-Life Problem
Communication Barriers:
o People with hearing, speech, or vision disabilities face challenges in engaging
with others in professional, educational, and social settings.
o Traditional video conferencing platforms lack inclusive features for these
groups.
Statistics:
o Globally, 430 million people have disabling hearing loss (WHO, 2021).
o Around 39 million people are blind, and 295 million have moderate to severe
vision impairment (WHO, 2021).
o Approximately 70 million people use sign language worldwide, yet
interpretation is often unavailable.
3. Pre-Existing Technology
Current Solutions:
o Subtitling/Captioning services (e.g., Zoom auto-captions).
o Text-to-speech systems (e.g., Google Text-to-Speech).
o Sign language detection tools in research but not widely implemented.
o Limited real-time, cross-modal communication platforms.
Limitations:
o Lack of integration across modes (audio, text, sign).
o Inaccessibility in low-resource settings due to high costs or technology gaps.
4. Patents and Research Landscape
Existing Patents:
o Real-time sign language recognition systems.
o Speech-to-text processing patents for video conferencing.
Ongoing Research:
o AI-based gesture recognition.
o Natural language processing for multilingual transcription.
o Real-time, low-latency video and audio processing.
5. Research Required
Sign Language Translation:
o Creating a comprehensive dataset for various sign languages.
o Enhancing real-time gesture recognition algorithms.
Audio-Text Conversion:
o Improving NLP models for accurate speech recognition in noisy
environments.
o Addressing multilingual complexities and dialect variations.
Text-to-Audio and Audio-to-Sign Conversion:
o High-fidelity text-to-speech engines for natural outputs.
o Integration of expressive gestures for natural sign language.
Multi-Modal Fusion:
o Combining audio, video, and text data streams.
6. Algorithms Required
Sign Language Recognition:
o Convolutional Neural Networks (CNNs) for video frame analysis.
o Recurrent Neural Networks (RNNs) or Transformers for temporal gesture
modeling.
Speech-to-Text:
o Automatic Speech Recognition (ASR) using Deep Learning (e.g., Wav2Vec,
Whisper).
Text-to-Speech:
o Neural TTS models (e.g., Tacotron, WaveNet).
Audio-to-Sign Conversion:
o Multimodal AI to map audio/text to gestures using Generative Adversarial
Networks (GANs) or Transformers.
7. How My Technology Solves the Problem
Real-Time Translation Across Modalities:
o Converts spoken language to sign language for deaf participants.
o Transcribes audio and displays text for hard-of-hearing users.
o Converts sign language into text/audio for hearing users.
Inclusive Design:
o Ensures accessibility for all participants, irrespective of disabilities.
Scalability and Flexibility:
o Integrates easily into existing video conferencing platforms.
Promoting Equality:
o Empowers users with disabilities to participate fully in conversations.
8. Conclusion
Vision: Breaking communication barriers for an inclusive world.
Impact: Making video conferencing universally accessible.
Call to Action: Support the development and adoption of this transformative
technology.
Let me know if you'd like more detailed slides on any of these topics!
