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Original Article

ATLAS-Audio Translation for Live Attraction System

Open Access Peer Reviewed Crossref DOI

Authors

  • Dr. Vinutha H P1
  • Ramya G S2
  • Sanjana S K3
  • Shrusti S P4
  • Vidya D V5

1 Professor, Department of Information Science and Engineering, Bapuji Institute of Engineering, Davangere, Karnataka, India. 2 3 4 5 Department of Information Science and Engineering, Bapuji Institute of Engineering, Davangere, Karnataka, India.

Article Information

Published Online
September-December 2026
Pages
503-509
Article Type
Original Article
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Abstract

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Tourists visiting unfamiliar regions often face language barriers that make it difficult to understand local attractions, historical landmarks, and cultural information. To address this challenge, this paper presents ATLAS (Audio Translation for Live Attraction Systems), a web-based application designed to facilitate multilingual communication through speech processing and language translation. The system integrates speech-to-text conversion, automatic language detection, text translation, and text-to-speech synthesis to transform spoken input into translated audio output. Users can provide audio through the application, obtain a textual transcription, translate the content into a selected target language, and listen to the translated speech. The system is developed using Python and Flask, with dedicated modules for audio processing, speech recognition, language detection, translation, and speech synthesis. In addition, ATLAS incorporates tourism-related information to help users explore destinations and access relevant attraction details in text and audio formats. By combining these capabilities in a centralized platform, the proposed system aims to improve the accessibility of tourism information for people who speak different languages. The application demonstrates the potential of integrating speech technologies and artificial intelligence into tourism support systems. Further evaluation is required to measure translation quality, speech-recognition accuracy, and response time across different languages and real-world acoustic conditions.

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  • CrossRef DOI
  • ISSN 2583-5300
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