Voice Chat Translation That Preserves Context and Emotion
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Solution Overview
Problem
Existing voice chat systems in virtual environments often lose context and emotion during language translation, relying on text-based translations that fail to capture the nuances of user interactions.
Innovation Solution
A system that converts user speech into text, retains context and emotion data, and generates translated speech using machine learning models to ensure accurate communication across different languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If text-based translation is used, then translation functionality is simple and easy to implement, but context and emotion are lost during translation
Solution Approach 1:
The patent introduces an intermediary processing layer that includes speech-to-text conversion, context extraction, and text-to-speech synthesis. This intermediary system bridges the gap between simple text translation and complex voice translation, enabling the system to process audio inputs, extract contextual information, and generate emotionally appropriate speech outputs without requiring direct complex voice-to-voice translation infrastructure.
Solution Approach 2:
The translation system is segmented into distinct functional modules: speech-to-text conversion module, context extraction module, translation module, and text-to-speech synthesis module. Each module handles a specific aspect of the translation process independently, making the overall system more manageable while enabling sophisticated processing of context and emotion at each stage.
2Reliability
If voice chat translation preserves context and emotion, then communication accuracy improves, but system complexity increases
Solution Approach 1:
The system employs a universal processing pipeline that handles multiple functions through a single integrated flow: speech recognition, context extraction, translation, and speech synthesis. This multi-functional approach allows the system to maintain high communication accuracy by preserving context and emotion while avoiding the need for separate specialized systems for each function, thereby managing overall complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where extracted context information is fed back into the translation and speech synthesis processes. This feedback loop ensures that context and emotional nuances are continuously refined and integrated into the final speech output, improving communication accuracy while the automated feedback processing helps manage system complexity through algorithmic optimization.
3Productivity
If simple word-for-word translation is used, then translation speed is fast, but meaning and nuance are lost
Solution Approach 1:
The system performs preliminary actions by extracting context data and emotional information from the speech input before the actual translation occurs. This preliminary analysis of context and emotion allows the translation system to prepare appropriate translation strategies that preserve meaning and nuance, enabling faster and more accurate translation by avoiding the need for slow, meticulous phrase-by-phrase analysis during the translation process itself.
Solution Approach 2:
The system changes key parameters in the translation process by transforming the input from raw audio to extracted context data and emotional parameters. This parameter transformation allows the system to work with condensed, processed information that maintains meaning and nuance while enabling faster translation speeds, as the system can directly translate the extracted parameters rather than processing every phonetic detail of the original speech.
Data Source
AI summary
Implementations described herein relate to methods, systems, and computer-readable media to provide automatic translation of voice chat in virtual experiences. The automatic translation may retain context data and/or emotion data extracted from input speech received from a first user. The context data and/or emotion data may be used in translating the input speech into a second language for output to a second user at a user device.