Cross-Language Communication for Tourists and Travelers

Abstract

Language barriers significantly impede global mobility and cultural exchange, especially for travelers. Although existing translation tools offer basic functionality, they often fail in real-time conversational contexts and lack travel-specific con- text awareness. This paper presents BridgeTalk, a comprehensive cloud-based communication tool designed to address these limitations. Our system integrates real-time speech recognition, Neural Machine Translation (NMT) via Google Cloud services, and text-to-speech synthesis into a unified pipeline. Key innovations include a dedicated ”Conversation Mode” featuring a chat-style interface (similar to messaging apps) for seamless bi-directional dialogue, a context-aware recommendation engine that suggests phrases based on the user’s location, and a robust history and favorites management system. The system is built as a cross- platform mobile application using React Native, providing an all- in-one solution for text, voice, and image-based translation with auto-detection capabilities. We evaluate our approach against baseline models, demonstrating superior utility in handling con- versational language and travel scenarios. The proposed system has practical applications in navigation, dining, shopping, and emergency situations, fostering smoother and more meaningful cross-cultural interactions.

Introduction

The text presents BridgeTalk, a smart, travel-focused translation system designed to overcome limitations of existing digital translation tools, such as high latency, lack of contextual awareness, and poor support for region-specific language needs. Aimed primarily at travelers and professionals abroad, BridgeTalk not only translates language but also provides context-aware assistance to enable smoother, real-time communication.

BridgeTalk integrates automatic speech recognition (ASR), neural machine translation (NMT), text-to-speech (TTS), and optical character recognition (OCR) within a React Native mobile application. Key features include a chat-style Conversation Mode for natural two-way dialogue, location-aware language recommendations (e.g., suggesting Telugu in Telangana), OCR-based translation for menus and signboards, and History and Favorites modules for efficient reuse of translations.

The system architecture uses a cloud-based, microservices approach, relying on Google Translate API and Google Cloud Vision for high translation accuracy and multilingual support, including regional Indian languages. By offloading computational tasks to the cloud, BridgeTalk maintains a lightweight device footprint while ensuring high performance.

Performance evaluation shows low latency for core features, with near-instant location-based language suggestions and acceptable real-time response times for translation, speech recognition, and OCR. Comparative analysis demonstrates that BridgeTalk offers advantages over standard translation apps, particularly in contextual awareness, dual-microphone conversation mode, and regional language mapping. Accuracy tests indicate 100% precision in identifying appropriate regional languages.

Conclusion

In conclusion, the BridgeTalk system demonstrates robust performance with minimal latency in its core recommendation engine. The integration of location-based context awareness distinguishes it from traditional translation tools, providing a more seamless user experience. Testing confirmed the system’s ability to accurately identify regional languages across 28 states and major cities, validating the proposed architecture.

References

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Copyright

Copyright © 2025 Para Upendar Naidu, Supradeep Varanasi, Sai Gargey Nakka, Janvi Govani, Durga Prasad Sunkari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.