Real-Time Media Translation Server for Multi-Language Distribution
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Solution Overview
Problem
Conventional language translation systems are costly and inefficient for real-time translation of a media feed from a speaker to multiple listeners speaking different languages, often requiring multiple human translators and introducing delays, and existing machine translation systems are complex and not capable of simultaneous real-time audio and text translation.
Innovation Solution
A computer-implemented technique that uses a server with processors to receive a media feed from a speaker device, determine preferred languages for listener devices, and provide machine-translated media feeds in real-time, utilizing unique identifiers for communication and short-range wireless transmission, enabling simultaneous translation of audio and text streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple human translators are used for real-time translation to multiple languages, then translation quality is improved, but cost and coordination complexity increase significantly
Solution Approach 1:
The patent combines multiple translation functions into a single machine translation system that can simultaneously translate to multiple languages. The server consolidates translation requests and processes them through machine translation APIs, eliminating the need for multiple human translators and reducing coordination complexity while maintaining real-time translation capability
Solution Approach 2:
The system creates translated copies of the media feed for each target language using machine translation. Instead of having multiple human translators work on the original content, the system generates automated translations that are distributed to listeners in different languages, significantly reducing cost and complexity
2Measurement precision
If human translators are used for language translation, then translation accuracy is improved, but translation speed and real-time capability deteriorate due to slight delays
Solution Approach 1:
The system performs preliminary actions by pre-loading translation models and maintaining ready-state translation capabilities on the server. When translation requests are received, the machine translation can immediately process the media feed without waiting for human translators to become available, achieving real-time translation while maintaining acceptable accuracy through modern machine translation APIs
3Device complexity
If a single server handles translation for multiple languages and devices, then device complexity is reduced, but processing load and computational requirements increase
Solution Approach 1:
The server acts as an intermediary between the media feed source and multiple listener devices. It receives the original media feed once, processes it through machine translation APIs for multiple target languages, and distributes the translated feeds to requesting devices. This centralized approach reduces overall system complexity while the server's computational load is managed through efficient request handling and translation optimization
Solution Approach 2:
The server is designed with multi-functionality to handle translation requests for multiple languages, multiple listener devices, and different media formats simultaneously. By consolidating these functions in a single server with universal translation capabilities, the system reduces the need for multiple specialized translation systems while managing computational resources efficiently through shared processing infrastructure
Data Source
AI summary
A computer-implemented technique can include receiving a media feed from a speaker computing device representing speech of a speaker user captured by the speaker computing device. The technique can include receiving a plurality of translation requests, each translation request being received from a listener computing device associated with a listener user and corresponding to a request to obtain a translated version of the media feed into a preferred language of the listener user. The technique can include determining the preferred language for each listener user. The technique can include obtaining a machine translated media feed for each of the translation requests, the machine translated media feed corresponding to a translation of the media feed from the source language to the preferred language of the listener user associated with the translation request. The technique can also include outputting the machine translated media feeds to the listener computing devices.