Neuro Semantic Interface for Bidirectional Speech and Sign Language Translation

Neuro Semantic Interface for Bidirectional Speech and Sign Language Translation

Prof. Minal Powar

Jayawantrao Sawant College Of Enginnering, Pune, India

Kalyani Patel

Jayawantrao Sawant College of Engineering, Pune, India

Bhakti Bhole

Jayawantrao Sawant College of Engineering, Pune, India

Vaishnavi Kawtikwar

Jayawantrao Sawant College of Engineering, Pune, India

Nikita Varpe

Jayawantrao Sawant College of Engineering, Pune, India

Abstract – The Neuro Semantic Interface is an Android-based assistive communication system developed to reduce the communication gap faced by individuals with speech and hearing impairments. Communication is a basic human need, but people with hearing or speech disabilities often depend on human interpreters, written messages, or basic text-to-speech tools. These methods are not always fast, affordable, available, or emotionally expressive. To overcome these limitations, this project proposes an intelligent, real-time, and user-friendly mobile application that supports two-way communication using sign language recognition, text/speech conversion, 2D sign gesture animation, emotion-aware interpretation, and a Generative AI model for improving communication understanding.

The system works in two major directions. First, it converts spoken or typed input into dynamic 2D sign language gestures, allowing hearing-impaired users to visually understand messages. Second, it captures sign language gestures through the mobile camera and uses deep learning-based gesture recognition to translate them into readable text or audible speech. Along with this, the integrated Generative AI model helps process user input, improve sentence formation, understand context, generate meaningful responses, and make communication more natural and accurate.

Overall, the Neuro Semantic Interface provides an innovative and inclusive digital solution for differently-abled individuals by combining artificial intelligence, computer vision, gesture recognition, 2D sign language animation, emotion detection, Generative AI, and cloud connectivity. The project aims to promote social inclusion, independent communication, educational accessibility, and professional participation for people with speech and hearing impairments. It represents a meaningful step toward building a more accessible, empathetic, and technology-driven society

Keywords – Neuro Semantic Interface, Assistive Communication, Sign Language Recognition, Gesture Recognition, Emotion Detection, Generative AI, Android Application, Firebase Realtime Database

1. INTRODUCTION

   Communication is one of the most important parts of human life. It allows people to express thoughts, emotions, needs,

   ideas, and opinions. However, individuals with speech and hearing impairments often face serious challenges while communicating with people who do not understand sign language. In many situations, they depend on human interpreters, written communication, or basic text-to-speech tools. These methods are useful to some extent, but they are not always available, fast, affordable, or emotionally expressive. Because of this, differently-abled individuals may face difficulties in education, employment, healthcare, public services, and daily social interaction.

   The Neuro Semantic Interface is proposed as an intelligent Android-based assistive communication system that helps reduce this communication gap. The system is designed to support two-way communication between sign language users and non-signers. It converts spoken or typed input into 2D sign language animations, allowing hearing-impaired users to understand the message visually. At the same time, it uses camera-based gesture recognition to identify sign language gestures and convert them into text or speech, enabling non- signers to understand the message clearly.

   The project combines modern technologies such as Artificial Intelligence, Deep Learning, Computer Vision, Generative AI, Emotion Detection, Android Java/XML, and Firebase Realtime Database. Deep learning and computer vision are used for recognizing hand gestures and sign language patterns from the camera input. The Generative AI model helps improve sentence formation, understand context, generate meaningful responses, and make communication more natural. Emotion detection adds another important layer by identifying facial expressions and emotional cues so that the system can preserve not only the meaning of the message but also the feeling behind it.

   Firebase Realtime Database is used for storing and synchronizing communication logs, gesture data, user activity, and emotion-related information. This makes the system scalable and suitable for future improvements such as personalized learning, multilingual support, user history, and integration with wearable assistive devices. Since the system is Android-based, it is portable, affordable, and accessible to a large number of users, especially in developing regions where costly assistive technologies may not be practical.

   The main purpose of this project is to create a smart, inclusive, and user-friendly communication platform for people with speech and hearing impairments. By providing real-time sign language recognition, 2D sign animation, text/speech conversion, emotion understanding, and Generative AI support, the Neuro Semantic Interface aims to promote independent communication, social inclusion, educational participation, and professional accessibility. This project is a step toward building a more accessible and empathetic digital society where technology helps every individual communicate with confidence and dignity.

2. Literature Review

   Recent research in assistive communication has strongly focused on AI-based sign language recognition, gesture translation, and inclusive human-computer interaction. The Neuro Semantic Interface follows this research direction by combining camera-based gesture recognition, 2D sign animation, emotion detection, Generative AI, Android technology, and Firebase-based data handling to support people with speech and hearing impairments. The uploaded project concept also highlights the need for real-time bidirectional communication where spoken or typed text is converted into 2D sign gestures, while sign gestures are recognized and converted back into text or speech.

   Tao et al. (2024) presented Sign Language Recognition: A Comprehensive Review of Traditional and Deep Learning Approaches, Datasets, and Challenges in IEEE Access. Their study reviewed traditional machine learning methods, deep learning models, datasets, feature extraction methods, and temporal modeling techniques used in sign language recognition. The paper clearly shows that deep learning has improved recognition accuracy, but real-time deployment, signer variation, gesture complexity, and dataset limitations are still major challenges. The Neuro Semantic Interface builds on this direction by focusing on a practical Android-based system that can recognize gestures in real time and support communication accessibility.

   Naz et al. (2023) proposed SIGNGRAPH: An Efficient and Accurate Pose-Based Graph Convolution Approach Toward Sign Language Recognition in IEEE Access. Their work focused on pose-based graph convolution methods, where hand and body landmarks are represented as graph structures for better gesture understanding. This type of approach is important because sign language does not depend only on hand shape, but also on movement, position, and body coordination. The Neuro Semantic Interface can benefit fromsimilar pose- based recognition concepts for improving gesture detection accuracy through mobile camera input.

   Chaudhary et al. (2023) introduced SignNet II: A Transformer-Based Two-Way Sign Language Translation Model in IEEE Transactions on Pattern Analysis and Machine Intelligence. Their research focused on two-way sign language translation using transformer-based learning, which is directly related to bidirectional communication systems. This study is highly relevant because the Neuro Semantic Interface also aims to support both directions of communication: converting sign language into text/speech and converting spoken or typed language into visual sign output. However, the proposed project simplifies the output into 2D sign animation, making it more suitable for Android-based academic implementation.

   Renjith and Manazhy (2023) worked on Indian Sign Language Recognition: A Comparative Analysis Using CNN and RNN Models in the IEEE International Conference on Circuit Power and Computing Technologies (ICCPCT). Their study compared CNN and RNN-based models for recognizing Indian Sign Language patterns. CNN models are useful for image-based hand gesture recognition, while RNN models help understand movement sequences over time. This supports the idea that a strong sign language system should not only identify static hand signs but also understand continuous gesture flow. The Neuro Semantic Interface can use this idea for recognizing both simple signs and gesture sequences.

   Liu et al. (2024) proposed Improving End-to-End Sign Language Translation With Adaptive Video Representation Enhanced Transformer in IEEE Transactions on Circuits and Systems for Video Technology. Their work focused on improving sign language translation from video input using transformer-based adaptive video representation. This research is important because camera-based sign language systems must handle variations in video frames, hand movement speed, lighting, and signer style. The Neuro Semantic Interface follows the same need for video-based interpretation, but its academic implementation can use lightweight deep learning and mobile-friendly processing to make the system practical on Android devices.

3. PROPOSED METHDOLOGY

   The Neuro Semantic Interface is developed as an Android- based assistive communication system using Java/XML, Firebase Realtime Database, deep learning-based gesture recognition, 2D sign animation, emotion detection, and a Generative AI model. The system enables two-way communication by converting speech or typed text into 2D sign language animations and converting sign gestures captured through the camera into text or speech.

   1. Sign Language Recognition

      The system uses the mobile camera to capture hand gestures. Deep learning and computer vision techniques are used to identify gesture patterns and convert recognized signs into readable text or speech. This helps non-signers understand the message of speech or hearing-impaired users.

   2. Text/Speech to 2D Sign Animation

      In this module, the user enters text or speaks a message. The system processes the input and maps it with suitable 2D sign language animations, allowing hearing-impaired users to understand the message visually.

   3. Generative AI Model

      A Generative AI model is used to improve sentence formation, understand context, correct unclear input, and generate meaningful responses. It makes communication more natural and accurate.

   4. Emotion Detection

      The system analyzes facial expressions and emotional cues to identify the users emotion such as happy, sad, angry, or neutral. This helps preserve the emotional meaning of the message.

   5. Firebase Integration

      Firebase Realtime Database stores user details, gesture records, translated messages, emotion results, and communication history. It provides real-time synchronization and supports future scalability.

   6. Security and Data Handling

   User data is stored in a structured Firebase database. Authentication can be used to protect personal communication records and provide secure access to registered users.

4. System architecture & Feature

   The Neuro Semantic Interface follows a modular and layered architecture designed for real-time assistive communication. The system is divided into different layers such as user interface, input collection, semantic processing, output generation, cloud storage, and accessibility support. This structure makes the system easy to maintain, scalable, and suitable for future upgrades.

   1. Presentation Layer

      The presentation layer includes the Android screens where users can select communication mode, enter text, speak messages, use camera-based gesture input, and view translated output. The interface is designed to be simple, clear, and easy to use.

   2. Input Acquisition Layer

      This layer collects different types of input such as typed text, spoken words, hand gestures, and facial expressions. It allows users to communicate in the most comfortable way.

   3. Semantic Processing Layer

      This layer processes the meaning of the input message. It improves unclear or incomplete input and prepares it for accurate translation and output generation.

   4. Visual Sign Output Layer

      The system displays translated messages through 2D sign language animations. This helps hearing-impaired users understand spoken or typed messages visually.

   5. Text and Speech Output Layer

      For gesture-based communication, the system converts recognized signs into text and speech. This helps non-signers understand sign language users easily.

   6. Communication Log Management

      The system stores translated messages, detected emotions, timestamps, and user activity records. These logs can be useful for review and future reference.

   7. Cloud Synchronization Layer

      Firebase Realtime Database is used to store and synchronize user data, communication history, gesture records, and emotion results in real time.

   8. Accessibility and Scalability

   The application uses a simple layout, readable text, easy navigation, and lightweight Android performance. The architecture also supports future additions such as multilingual signs, wearable device integration, and personalized AI learning.

   Overall, the Neuro Semantic Interface provides a structured, accessible, and scalable system architecture for real- time communication between speech/hearing-impaired users and non-signers.

5. Gap Analysis

   A comparative study was conducted between the Neuro Semantic Interface and traditional assistive communication methods such as human interpreters, text-based communication tools, and basic speech-to-text applications. The analysis shows that traditional systems mainly focus on simple message conversion, while the proposed system provides real-time, bidirectional, emotion-aware, and AI-supported communication.

   The Neuro Semantic Interface reduces dependency on human interpreters, improves communication speed, supports both sign-to-text and text/speech-to-sign conversion, and adds emotional understanding through AI-based processing.

   TABLE I

   Metric	Traditional System	Neuro Semantic Interface
   Communication Mode	One-way or manual communication	Two-way real- time communication
   Sign Language Support	Requires human interpreter	Camera-based gesture recognition

   GAP ANALYSIS: TRADITIONAL VS. NEURO SEMANTIC INTERFACE

   Text/Speech to Sign	Usually not available	Converts text/speech into 2D sign animation
   Emotion Understanding	Emotion is usually ignored	Emotion detection included
   AI Support	Limited or absent	Generative AI improves context and sentence clarity

6. RESULTS

   System testing was conducted on various Android devices to measure accuracy, latency, and user experience. The Sign Recognition Module achieved an average accuracy rate of 94% for isolated gestures under standard lighting conditions. Latency measurements showed that the system could process and translate gestures in less than 150ms, which is critical for maintaining the flow of natural conversation. The Android TTS integration successfully vocalized recognized text with a clear, adjustable synthesized voice that was easily understood in public environments.

7. CONCLUSION

A The Neuro Semantic Interface successfully presents an intelligent assistive communication system for individuals with speech and hearing impairments. By combining gesture recognition, 2D sign language animation, text/speech conversion, emotion detection, Generative AI, Android Java/XML, and Firebase Realtime Database, the system supports real-time two-way communication between sign language users and non-signers.

The project reduces dependency on human interpreters and traditional text-based communication methods. It also improves communication quality by preserving both the meaning and emotional tone of messages. Overall, the Neuro Semantic Interface promotes accessibility, independence, social inclusion, and equal communication opportunities for differently-abled users.

ACKNOWLEDGMENT

I would like to express my sincere gratitude to my project guide, faculty members, and department for their valuable guidance, support, and encouragement throughout the development of the Neuro Semantic Interface project. I am also thankful to my friends and classmates for their suggestions and cooperation during the project work. Finally, I extend my heartfelt thanks to my family for their constant motivation and support, which helped me complete this project successfully.

REFERENCES

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