A GenAI-Based Approach for Real-Time Multilingual In-Flight Communication Systems.

2026-26-0784

To be published on 06/01/2026

Authors
Abstract
Content
In today’s global aviation industry, passenger experience is strongly influenced by effective communication. In-flight announcements, often limited to English and a single local language, can create confusion and stress for international travelers who may not be fluent in either. This communication gap not only impacts passenger comfort but also poses potential risks in conveying time-sensitive or safety-critical information. Recent advances in Generative Artificial Intelligence (GenAI), particularly in speech recognition, neural machine translation, and naturalistic text-to-speech, provide a pathway to overcome these challenges. This paper explores the concept of real-time multilingual in-flight announcements, delivered in each passenger’s preferred language through connected headphones or personal devices. The proposed system architecture integrates speech-to-text conversion, language translation, and speech synthesis with aircraft infotainment platforms. Potential applications range from short-term solutions such as pre-generated multilingual announcements and Security instruction captions to long-term visions of fully personalized, real-time translations with minimal latency. The benefits include improved inclusivity, accessibility for hearing-impaired passengers, and enhanced brand differentiation for airlines. At the same time, challenges such as regulatory certification, translation accuracy, latency constraints, and hardware integration must be addressed. Beyond aerospace, this capability has cross-domain relevance in automotive, railways, and public services, making it a promising area for future customer experience innovations.
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Citation
Mishra, A., Kature, K., and Patil, A., "A GenAI-Based Approach for Real-Time Multilingual In-Flight Communication Systems.," SAE Technical Paper 2026-26-0784, 2026, .
Additional Details
Publisher
Published
To be published on Jun 1, 2026
Product Code
2026-26-0784
Content Type
Technical Paper
Language
English