Multi-Platform Voice Translation With Context-Aware Engine Selection
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Solution Overview
Problem
Existing voice translation technologies face challenges in providing high-quality, accurate translations across multiple platforms and languages, particularly in real-time communication sessions with multiple speakers, due to inconsistent speech contexts and language changes.
Innovation Solution
An AI-driven multi-platform translation system that utilizes a voice analyzer and translator with integration layers, diarization, and machine learning models to separate audio streams, select optimal translation engines, and continuously monitor speech contexts for near-real-time, accurate translations, supporting various communication platforms and languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If voice translation uses multiple APIs and speech recognition libraries across different platforms, then platform compatibility is improved, but translation accuracy and consistency deteriorate due to inconsistent speech contexts
Solution Approach 1:
The patent introduces a centralized voice analysis system as an intermediary between multiple communication platforms and translation engines. This intermediary standardizes the processing of audio streams across different platforms (Zoom, Teams, Webex, Skype) before translation, ensuring consistent speech context analysis and improving translation accuracy while maintaining multi-platform compatibility.
Solution Approach 2:
The system segments the voice translation process into distinct modules: audio stream separation (diarization), speech context analysis, translation engine selection, and translation execution. This segmentation allows each module to specialize in specific tasks, improving overall accuracy while maintaining compatibility with multiple platforms through standardized interfaces.
2Quantity of substance
If the system processes multiple audio streams from multiple speakers, then translation completeness is improved, but processing complexity and time increase
Solution Approach 1:
The patent applies diarization technology to segment multiple audio streams from different speakers into separate channels. This segmentation identifies and isolates each speaker's audio, allowing the system to process them individually through translation engines, thereby maintaining translation completeness while managing processing complexity through structured organization.
Solution Approach 2:
The system dynamically adjusts its processing based on the number and complexity of audio streams detected. When multiple speakers are identified, the system activates enhanced diarization and context analysis modes, while adapting the translation engine selection based on the specific speech contexts detected, optimizing processing resources accordingly.
3Speed
If the system translates in real-time during communication sessions, then responsiveness is improved, but translation accuracy deteriorates due to inconsistent speech contexts and language changes
Solution Approach 1:
The system performs preliminary speech context analysis before translation occurs. By analyzing the speech context in advance and continuously monitoring for changes, the system can select the most appropriate translation engine beforehand, ensuring both speed and accuracy. This preliminary action allows real-time translation while maintaining high accuracy through pre-computed translation strategies.
Solution Approach 2:
The system continuously monitors speech contexts during real-time translation and uses feedback to dynamically adjust translation engine selection. When language changes or context shifts are detected, the system receives feedback and switches to more suitable translation engines, maintaining both responsiveness and accuracy throughout the communication session.
Data Source
AI summary
An Artificial Intelligence (AI) Driven multi-platform, multi-lingual translation system analyzes the speech context of each audio stream in the received audio input, selects one of a plurality translation engines based on the speech context, and provides translated audio output. If audio input from multiple speakers is provided in a single channel then it is diarized into multiple channels so that each speaker transmitted on a corresponding channel to improve audio quality. A translated textual output received from the selected translation engine is modified with sentiment data and converted into an audio format to be provided as the audio output.